{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1MTl4yEAS2Lp"
   },
   "source": [
    "### Build anCNN for ECG Signal Classification (5 classes)  - Keras\n",
    "This is the implementation of an CNN for classifying the ECG signals. <br>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: tensorflow in /Users/antoniogondim/anaconda3/lib/python3.10/site-packages (2.14.0)\n",
      "Requirement already satisfied: tensorboard<2.15,>=2.14 in /Users/antoniogondim/anaconda3/lib/python3.10/site-packages (from tensorflow) (2.14.1)\n",
      "Requirement already satisfied: ml-dtypes==0.2.0 in /Users/antoniogondim/anaconda3/lib/python3.10/site-packages (from tensorflow) (0.2.0)\n",
      "Requirement already satisfied: packaging in /Users/antoniogondim/anaconda3/lib/python3.10/site-packages (from tensorflow) (22.0)\n",
      "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /Users/antoniogondim/anaconda3/lib/python3.10/site-packages (from tensorflow) (0.5.4)\n",
      "Requirement already satisfied: termcolor>=1.1.0 in /Users/antoniogondim/anaconda3/lib/python3.10/site-packages (from tensorflow) (2.3.0)\n",
      "Requirement already satisfied: opt-einsum>=2.3.2 in /Users/antoniogondim/anaconda3/lib/python3.10/site-packages (from tensorflow) (3.3.0)\n",
      "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /Users/antoniogondim/anaconda3/lib/python3.10/site-packages (from tensorflow) (0.34.0)\n",
      "Requirement already satisfied: wrapt<1.15,>=1.11.0 in /Users/antoniogondim/anaconda3/lib/python3.10/site-packages (from tensorflow) (1.14.1)\n",
      "Requirement already satisfied: flatbuffers>=23.5.26 in /Users/antoniogondim/anaconda3/lib/python3.10/site-packages (from tensorflow) (23.5.26)\n",
      "Requirement already satisfied: google-pasta>=0.1.1 in /Users/antoniogondim/anaconda3/lib/python3.10/site-packages (from tensorflow) (0.2.0)\n",
      "Requirement already satisfied: numpy>=1.23.5 in /Users/antoniogondim/anaconda3/lib/python3.10/site-packages (from tensorflow) (1.23.5)\n",
      "Requirement already satisfied: tensorflow-estimator<2.15,>=2.14.0 in /Users/antoniogondim/anaconda3/lib/python3.10/site-packages (from tensorflow) (2.14.0)\n",
      "Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in /Users/antoniogondim/anaconda3/lib/python3.10/site-packages (from tensorflow) (4.25.0)\n",
      "Requirement already satisfied: astunparse>=1.6.0 in /Users/antoniogondim/anaconda3/lib/python3.10/site-packages (from tensorflow) (1.6.3)\n",
      "Requirement already satisfied: absl-py>=1.0.0 in /Users/antoniogondim/anaconda3/lib/python3.10/site-packages (from tensorflow) (2.0.0)\n",
      "Requirement already satisfied: h5py>=2.9.0 in /Users/antoniogondim/anaconda3/lib/python3.10/site-packages (from tensorflow) (3.7.0)\n",
      "Requirement already satisfied: six>=1.12.0 in /Users/antoniogondim/anaconda3/lib/python3.10/site-packages (from tensorflow) (1.16.0)\n",
      "Requirement already satisfied: grpcio<2.0,>=1.24.3 in /Users/antoniogondim/anaconda3/lib/python3.10/site-packages (from tensorflow) (1.59.2)\n",
      "Requirement already satisfied: libclang>=13.0.0 in /Users/antoniogondim/anaconda3/lib/python3.10/site-packages (from tensorflow) (16.0.6)\n",
      "Requirement already satisfied: keras<2.15,>=2.14.0 in /Users/antoniogondim/anaconda3/lib/python3.10/site-packages (from tensorflow) (2.14.0)\n",
      "Requirement already satisfied: setuptools in /Users/antoniogondim/anaconda3/lib/python3.10/site-packages (from tensorflow) (65.6.3)\n",
      "Requirement already satisfied: typing-extensions>=3.6.6 in /Users/antoniogondim/anaconda3/lib/python3.10/site-packages (from tensorflow) (4.4.0)\n",
      "Requirement already satisfied: wheel<1.0,>=0.23.0 in /Users/antoniogondim/anaconda3/lib/python3.10/site-packages (from astunparse>=1.6.0->tensorflow) (0.38.4)\n",
      "Requirement already satisfied: google-auth-oauthlib<1.1,>=0.5 in /Users/antoniogondim/anaconda3/lib/python3.10/site-packages (from tensorboard<2.15,>=2.14->tensorflow) (1.0.0)\n",
      "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /Users/antoniogondim/anaconda3/lib/python3.10/site-packages (from tensorboard<2.15,>=2.14->tensorflow) (0.7.2)\n",
      "Requirement already satisfied: werkzeug>=1.0.1 in /Users/antoniogondim/anaconda3/lib/python3.10/site-packages (from tensorboard<2.15,>=2.14->tensorflow) (2.2.2)\n",
      "Requirement already satisfied: requests<3,>=2.21.0 in /Users/antoniogondim/anaconda3/lib/python3.10/site-packages (from tensorboard<2.15,>=2.14->tensorflow) (2.31.0)\n",
      "Requirement already satisfied: google-auth<3,>=1.6.3 in /Users/antoniogondim/anaconda3/lib/python3.10/site-packages (from tensorboard<2.15,>=2.14->tensorflow) (2.23.4)\n",
      "Requirement already satisfied: markdown>=2.6.8 in /Users/antoniogondim/anaconda3/lib/python3.10/site-packages (from tensorboard<2.15,>=2.14->tensorflow) (3.4.1)\n",
      "Requirement already satisfied: pyasn1-modules>=0.2.1 in /Users/antoniogondim/anaconda3/lib/python3.10/site-packages (from google-auth<3,>=1.6.3->tensorboard<2.15,>=2.14->tensorflow) (0.2.8)\n",
      "Requirement already satisfied: rsa<5,>=3.1.4 in /Users/antoniogondim/anaconda3/lib/python3.10/site-packages (from google-auth<3,>=1.6.3->tensorboard<2.15,>=2.14->tensorflow) (4.9)\n",
      "Requirement already satisfied: cachetools<6.0,>=2.0.0 in /Users/antoniogondim/anaconda3/lib/python3.10/site-packages (from google-auth<3,>=1.6.3->tensorboard<2.15,>=2.14->tensorflow) (5.3.2)\n",
      "Requirement already satisfied: requests-oauthlib>=0.7.0 in /Users/antoniogondim/anaconda3/lib/python3.10/site-packages (from google-auth-oauthlib<1.1,>=0.5->tensorboard<2.15,>=2.14->tensorflow) (1.3.1)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /Users/antoniogondim/anaconda3/lib/python3.10/site-packages (from requests<3,>=2.21.0->tensorboard<2.15,>=2.14->tensorflow) (3.4)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/antoniogondim/anaconda3/lib/python3.10/site-packages (from requests<3,>=2.21.0->tensorboard<2.15,>=2.14->tensorflow) (1.26.14)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /Users/antoniogondim/anaconda3/lib/python3.10/site-packages (from requests<3,>=2.21.0->tensorboard<2.15,>=2.14->tensorflow) (2023.5.7)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /Users/antoniogondim/anaconda3/lib/python3.10/site-packages (from requests<3,>=2.21.0->tensorboard<2.15,>=2.14->tensorflow) (2.0.4)\n",
      "Requirement already satisfied: MarkupSafe>=2.1.1 in /Users/antoniogondim/anaconda3/lib/python3.10/site-packages (from werkzeug>=1.0.1->tensorboard<2.15,>=2.14->tensorflow) (2.1.1)\n",
      "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /Users/antoniogondim/anaconda3/lib/python3.10/site-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.15,>=2.14->tensorflow) (0.4.8)\n",
      "Requirement already satisfied: oauthlib>=3.0.0 in /Users/antoniogondim/anaconda3/lib/python3.10/site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard<2.15,>=2.14->tensorflow) (3.2.2)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install tensorflow\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-11-22 11:16:43.850394: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.14.0\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "SOxMxEdsS-Au"
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.optimizers import Adamax\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython import display\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kQOrBDYmOZjK"
   },
   "source": [
    "### Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "RsFnMl7ROZjK"
   },
   "outputs": [],
   "source": [
    "X=pd.read_csv('ECG_dataX.csv')\n",
    "Y=pd.read_csv('ECG_dataY.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hi\n"
     ]
    }
   ],
   "source": [
    "print('hi')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "[5 rows x 187 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('ECG_dataX.csv').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class_label</th>\n",
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       "  </thead>\n",
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      ],
      "text/plain": [
       "   class_label\n",
       "0            0\n",
       "1            0\n",
       "2            0\n",
       "3            0\n",
       "4            0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('ECG_dataY.csv').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6t4WUhbDOZjL",
    "outputId": "f6a88fc0-d45f-46ca-cbc9-7564214ca087"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3841, 187)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#convert dataframe to numpy array\n",
    "X=X.values\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VwmqQyBZOZjM",
    "outputId": "75a5187c-a678-40bb-9200-974bb5d188b5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3841, 1)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#convert dataframe to numpy array\n",
    "Y=Y.values\n",
    "Y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9P_7x-_oOZjM",
    "outputId": "94c3c02f-6cc7-4e7b-833b-1f47798a384b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3841,)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reshape Y into a 1D array\n",
    "Y=Y.reshape(-1)\n",
    "Y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 317
    },
    "id": "wYaJ9_X3QDPI",
    "outputId": "bacb0867-25e6-4150-ab77-a4b6264346f6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([800.,   0., 800.,   0.,   0., 800.,   0., 641.,   0., 800.]),\n",
       " array([0. , 0.4, 0.8, 1.2, 1.6, 2. , 2.4, 2.8, 3.2, 3.6, 4. ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "pLlBWI0ROzNo"
   },
   "outputs": [],
   "source": [
    "fs=125  # sampling frequency\n",
    "Ts=1/fs # sampling interval\n",
    "N=187 # the number of timepoints\n",
    "Duration=N*Ts # duration of a signal\n",
    "t=np.linspace(0, Duration-Ts, N) # array of timepoints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 745
    },
    "id": "sXzrl7-PO00f",
    "outputId": "dcd21048-a382-4e94-b805-ae1b0c7dfa1d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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/ceIEkyZNajOQB9Dr9RQXFzt7GnG64z9B6g+g1sLCZ0ClArUGvMIACLKWkJwh6+aFEEL8htls8MvTTe/rwMy8PcV+dNBogvRBDPIZBMChkoGZav/0qiOUG80MC/Pmiz9MxdNVy69ppdy9fC8Wa+uTKkIIIfoGp4N5jUaD2dx+KlZ2djYeHh7OnkacTuMC/oNg0u8hKNFxt8onAoBwVbGsmxdCCPHblr4JMrYo75mDzlLuM+S1u5s9mJ8aphTQHR6gzOQPxGA+OaOMFbuUtrxPXDSC0ZG+vLVkPC4aNasP5fPwNwfbzJIUQgjR+5wO5gcPHsy+ffuor69vdZuqqir279/PsGHDnD2NOF3cLLhjO8x5qOn93kowH6YqYftJCeaFEEL8htnXyo9bAhHjle+rClrfHrBYLfyar3TfmRquBPMjA0cCcLB4YK2br7dYefhr5TldPj6SCbH+AEwbHMhLV49FpYJlv2by4db0XhylEEKI9jgdzF944YXk5eXxz3/+s9Vt/vnPf1JRUcEll1zi7GlES7Su4HJatoNPJADhqlL2Z1dQXdv6RRYhhBBiwErbBBmblVn5Gfc4lqG1NzOfUppCRW0FXjovRxA/ImAEoFS0H0iz1J9sz+BwXiXebloeWDi0yWOLRoVx77lK5t83+3J7Y3hCCCE6yOlg/p577iEiIoInnniCiy++mGXLlgFQUFDAl19+ydVXX82zzz5LbGwst99+e7cNWLSiIZgf5FJOvdXGLlk3L4QQ4rfIvlY+abHy3ugVqvzczpp5e4r9xNCJaNVKEdlE/0S0Ki1ltWXkVbefpt8fFBpMPP/jUQDuWzCUQE/XZtvMH6H8zo7kGWTtvBBC9GFOB/O+vr6sXr2auLg4vv32WxYvXoxKpWL16tVcccUVfPbZZ0RHR/Pdd9/JmvluZLaaufeXe0kuSG76QEOa/WDXcgBJtRdCCPHbk75ZWS+v1sHMpcp9nh0M5vMa1ss3pNgDuGpcifeLBwZOqv27m9Mw1NYzKsKHaydFt7hNXKAH7joNNWYLacXVZ3iEQgghOsrpYB5g+PDhHDx4kNdee43zzjuPYcOGkZiYyLx583jhhRc4dOgQw4e33QZGdM6KIytYk76GG1ffyF83/pWC6oY1gA0F8IKsSucAKYInhBDiN2fDv5Sv4xY7MtaazMy3kipvNBvZU6i0tz01mAcYEdiQaj8AiuDZbDZWH1Quatw+ezAatarF7TRqFUPDvAA4nFd5xsYnhBCic7rcjNzNzY3bb79dUunPkPMGncfJipN8cfQLVqat5Oesn7l9zO0sjl6ADnCrLUZHPQdyKqiqrcfTVfrNCyGE+A1I39I4Kz9jaeP9niHKV6sZjKXgEdBs110Fu6i31hPhGUG0V9PZ6hEBI/iCLzhU3P+D+WOFVWSUGHHRqpmdGNTmtsPDvNmTWc7h3EouHBN+hkYohBCiM7o0My/OPD83Px6Z+gifnv8po4NGU1Nfw4vJL3L5+j9QqXVDhY0kXyMWq41d6aW9PVwhhBDizPilYVY+6XrwjWq8X+sC+kDl+1aK4NnXy08Jm4JK1XS22l4M73DJYaw2a/eO+Qz78ZAyKz9jSGC7F/tHhPsAcCi3osfHJYQQwjkSzPdTIwJG8PHCj/nn9H/i6+rLyYqT/BigpBKeFVoHwDZZNy+EEOK3IGMbpG1sulb+VO0Uwduetx1onmIPMNh3MK4aVwxmA5mVmd025N7w42Flad65w0Pa3XZ4uDcAh3MrB1QlfyGEGEg6nIM9aNAgp0+iUqk4ceKE0/uLlqlVai4achH7i/bz2dHPyHNX1rdN9K8BYLusmxdCCPFb4JiVvw58Wyjq5hUKBQehqnkwX1Nfw4ly5TPKuOBxzR7XqXUk+ieyv2g/h0oOEesT250jP2Nyy2vYn12BSgVzh7UfzCeGeKFWQUl1HUWGWoK93c7AKIUQQnRGh4P59PT0Fu9XqVStXrG1P3Z6yproXqEeyoxDvovSXiberQKI4nBeJWaLFZ1GEjCEEEIMUJnb4eQGUGubrpU/lWNmvnma/cnyk9iw4efqR6B7YIu7jwgYwf6i/RwsPsh5g87rpoGfWT+lKLPy46P9CPJq3o7udO4uGgYHeXKssIpDuZUSzAshRB/U4SgvLS2t2W3pUuVN86KLLuLLL79kz5497Nmzh6+++oqLL74YgKVLl3Ly5MkeGbxQOIL5hlfTp64AT1ctZouNk0XSUkYIIcQAZq9gP/Y68ItpeRuvMOVrC2n2R8uUnuvxfvGtTj6cum6+v/rxUEOK/Yj2Z+XtHKn2UtFeCCH6pA7PzMfENH2D/Pbbb3nxxRf55JNPuOaaa5o8NmbMGC666CKWL1/Oddddx4wZM5rtL7pPmIfyISXfpqyVV1XmkBDiye7MclILDCSGevXm8IQQQoiekfkrnPxZmZWf+ZfWt7NXtG8hmD9WfgzA0U++JSMClPZ0KaUp1Fvr0ar7V6eYCqOZ7Q11dM4ZHtrh/YaHefPN3lwO50owL4QQfZHT+dfPPfccEyZMaBbIn+rqq69mwoQJPP/8886eRnSAY2beXIUNoCKHxFDlanpqvrwBCyGEGKDsa+XHXtv6rDx0bGbet/VgPtY7Fr1WT019DWkVaU4Pt7f8nFpIvdVGQogncYEeHd5PKtoLIUTf5nQwv3//fuLjW3/js4uPj2f//v3OnkZ0QIg+BBUq6mz1lKrVUJnN0IbZ+NR8Qy+PTgghhOhmVgus/hucWN/+rDy0GcwfK2t/Zl6j1pDglwDgKJbXn/x4WHne53ZiVh5gWJjyWSK9xEhVbX23j0sIIUTXOB3MW63WDlWoP3HihLQ06WE6jc5RtCdfq4WaMob6awBILZBgXgghxABSWwXLr4Ptryo/z3sM/GLb3sdeAK8qH6yNveJLakooNZWiQsUQ3yFtHiJIH6TsY+pfnWJMZgsbUouAzq2XBwjwdCW0ofDdEVk3L4QQfY7TwXxSUhI7duzgq6++anWbr7/+ml9//ZWkpCRnTyM6yLFu3s0TgKF65U03q7RGrqYLIYQYGCpy4P0FcHQVaFzh8vdg2p/a388zGFCBtR6MjcG4fb18pFckep2+zUMEuAUAygWA/mTriWKMdRbCfNwYFeHT6f1HNBTBOyTr5oUQos9xOpi/7777sNlsXHnllVx33XWsXLmSlJQUUlJSWLVqFddffz1XXnklKpWK++67rzvHLFoQ4qFcbc/z9AfAx1xAcEPrmaMyOy+EEKK/KzgE78yF/AOgD4Qbv4eRl3VsX40OPBrazp3Sa96RYt/Genk7ewZcf5uZd1SxHx7iVKtgR0V7CeaFEKLPcboc6/nnn8/zzz/P/fffz/Lly1m+fHmTx202GxqNhqeffprzzz+/ywMVbWucmW8obFORQ2JoAoWGWo7mGxgX7deLoxNCCCG6aPWDSp/4oKFw7Yr2U+tP5xUK1UXKuvnQUUDH1svbBbj3v5l5i9Xm6C9/7ojOrZe3Gx7WMDOfJ0XwhBCir3F6Zh7gnnvuYffu3dx8880MHjwYV1dXXF1dGTRoEDfffDO7du3i3nvv7a6xijbYK9rnaRuuz1TmkBiiFK45IkXwhBBC9GeWesjeqXx/+fudD+ThlCJ4eY67Tu0x3x57mn1xTXHnz91L0oqrKK6qQ++iYVKcv1PHsFe0P5pfhdlibWdrIYQQZ1KXG6WOGjWKt99+uzvGIrrAMTOvbig2WJFNQoQSzEuavRBCiH6t8DCYjeDqrczMO+O0XvMWq8VRmd5eqb4tjpn5fpRmf7SgCoCEEC90GufmbyL93PFy1WKoredEURVDG1rfCiGE6H1dmpkXfYej17zFpNxRIe3phBBCDBD2WfmI8aB28qPLae3psquyMVlMuGpcifaKbnf3U9Ps+0uXHvv7vz1TzxlqtYph9iJ4ObJuXggh+hIJ5gcIezBfVF+NGaAyh/hgL1QqKKmuo8hQ26vjE0IIIZyWvUv5GjnR+WPY29M1BPP29fKDfAahUWva3d2eZm+2mjGY+8dFcntmXnyIZ5eOY183f1ja0wkhRJ/idJr9nDlzOrytSqVi3bp1zp5KdIC/mz86tQ6z1UyhVkNERQ7uOjUx/nrSS4wcLTAQ1FDdXgghhOhX7DPzXQrmm66Z70zxOwA3rRueOk+qzFWU1JTg7dL3081TG4L5xFDnZ+ZBKtoLIURf5XQwv2HDhna3UalU2Gw2p1qhiM5Rq9SEeoSSZcgiX6MlorYaTOUkhnqRXmLkSL6B6UMCe3uYQgghROfUlEGJEngTOcH543g1XTNv7zHfkfXydgHuAVSZqyiuKSbOJ875sZwBJrOF9OJqoGtp9nBqr/kK+VwnhBB9iNPB/M8//9zi/VarlYyMDL7//nu+/PJLHnzwQc4991ynByg6zh7M53n4QG1hQ3s6b9YcKiA1X66mCyGE6IdykpWv/oNA71xFdqBxZr6qAKzWTlWytwtwCyCjMqNfFME7WVSN1Qa+el2XM/Pig73QaVRUmurJKa8h0k/fTaMUQgjRFU4H87Nnz27z8RtvvJF///vf3H///Vx55ZXOnkZ0gqOivd4XSgsb2tONBiC1oaKtEEII0a90x3p5AI9gQAU2CzWV2WRWZgKdn5mH/tFr3r5ePiHYq8sz6S5aNYODPDmSbyAlzyDBvBBC9BE9WgDvz3/+M1FRUTz22GM9eRrRwFHR3rXhTbYiy7FO7liBAau1f1TfFUIIIRy6Y708gEYLnsEAnMxPxoYNP1c/R2G7jrBv2x+Ceft6+YTQrhW/s7N3yJF2t0II0Xf0eDX7MWPGsHnz5p4+jeCUYF7bUJW3IofYAD0uWjXGOgtZZcZeHJ0QQgjRSVbrKTPzXVgvb9fQa/5o8UFASbHvzKy1fWa+1FTa9bH0sKPd0JbuVAkSzAshRJ/T48F8aWkpVVWS4n0m2NPs87Aod1TmoNWoGRKkXJWXfvNCCCH6ldITYCoHrRuEjOz68RrWzR8rPwF0br08NAbzxTXFXR9LD0t1tKXrnmDeflFAPksIIUTf0aPB/MaNG9m0aRODBw/uydOIBqF6ZWY+z9IwA1+RAzSmxskbsBBCiH7FPisfNhY0uq4fr6HX/LFq5f2xM+vlof+k2VfX1pNdVgNAQnfNzDcc52RRNfUWa7ccUwghRNc4XQDvH//4R6uPGQwGUlJSWLNmDVarlVtvvdXZ04hOsKfZGywmqlUqPCqygMbUuCOSGieEEKI/cayX74YUe3DMzB+tVYLxeF/nZub7ejX7Y4VKRmSQlyv+Hi7dcswIX3f0LhqMdRbSS4wMCe6etfhCCCGc53Qw/9hjjzn6yLdGrVZz1113cffddzt7GtEJni6eeOm8MJgN5Gs1DDbkg83mKIJ3VGbmhRBC9CfdVfzOziuEErWaUpsZFSoG+3Yuc/DUmfm+3G+9u9fLA6jVKuJDvNiXVc7RAoME80II0Qc4Hcw/8sgjrb6Jubi4EBERwZw5c4iMjHR6cKLzQj1DMZQZyNdqGVxjgpoyR5r9yeJqaustuNoL5AkhhBB9VV01FBxSvu+2YD6MYy5Kun6kVyR6XedarNln5uusdVSZq/By6b5guTs1rpfv3oA7McSTfVnlpOYbWDQqrFuPLYQQovO6NDMv+p5QfSjHyo6R5+YFNSaoKiA0aChebloMpnpOFlUzLMy7t4cphBBCtC13L9gs4BUOPhHdc0yvUFJdlLTzzqbYA7hr3fHQeVBtrqa4prjPBvP2ivPdOTMPjevmpaK9EEL0DU4XwMvMzKS0tP3WLGVlZWRmZjp7GtFJ9or2+fqGgN2Qj0qlkiJ4A4TZauZwyWEOlxwmvSKdImMRRrMRq02KEQkhBpicbmxJZ+cVRrKbKwCjAp2rjt8fiuAddfSY75lgPlWCeSGE6BOcnpmPi4vjxhtv5N13321zu/vvv5/333+f+vp6Z08lOsFeBC/PRfmwQlUBAImhXuxMLyMlv5KL6aYZDnFGlJnK2JyzmY3ZG9mSuwVDXfMPUS5qF6K9o4n2iibGJ4YYrxg0ag3V5mqMZiPGeiNqlZpZkbMYHTi6z67zFEIIh+4ufgdY3P3Z5e4GwBSfzlWytwtwDyDTkNlni+CVG+soqKwFIL6b17Xba/BklBgxmS246WTZnhBC9Cang3mbzdZm8bvTtxVnhj2YL9A0vMEa8gEcqfUpeXI1vT8oNZWyNn0tq9JXsadwT5OZdy8XL9y17o4g3WqzUmet43j5cY6XH4es1o/71v63iPCMYEHsAhbGLSTBL0ECeyF6msWs3Fw6tz77N81mg6xuLn4HHCk/hkGtxstiZajGuUC3r8/MHy1QKtlH+Lrj5dYN7fxOEezlio+7jooaMyeLqhkeLsv2hBCiNzkdzHeUwWDAxaV72qKI9jlm5mnIhGiYmW8M5it7ZVyikT1VfnfBbqrN1eh1ejy0Huh1ekwWE+sy1rE9bzsWm8WxT6JfIrMiZzErchajAkehUSsXa2w2GyaLieKaYjIrM0mvTCejMoNMQyYqVHjoPNBr9XjoPCgxlbAhawM5VTm8e/Bd3j34LpNCJ/Hs7Gfxd/Pvpd+GEH1Q8XHYvxwOfwMhI+C8F0DvxL+R2irY9R5sfUUp5jblDzD9z+Dm0/1jHmgqc6AqH1Qapcd8N/k1/1cAxptMaKuLnDpGX29PZ0+BT+jm4ncAKpWKxBAvdqSXcrTAIMG8EEL0sh4L5q1WK4cOHWL9+vVER0f31GnEaRxr5q0mbICqYWZ+aKgXKhUUGWopMtQS5OXai6P87cmszGR1+mp25e9ib9Feaupr2t1nRMAIFsYt5NyYcwnzbLlqsEqlwl3rTpRXFFFeUUyPmN7mMWvqa9iYvZFVaavYlL2JHfk7uPaHa3llzivE+3W+GJQQA0ZtFez7FPYtb1yrDVB8FLJ2wKVvQ2zb/74cTBWw4y3Y9hrUnFJbZtNzsPMdmHEPTLpNZurbcvwn5WvoyG79Pe3I3wHAJFMtGPKcOoYjmO+rM/P5PbNe3i4+xJMd6aWybl4IIfqATgXzGk3TtVEffvghH374Ybv73XLLLZ0blXBaiD4EFSrqbBZK1WoCGoJ5vYuWuAAPThZXk5JXSZBXUC+PdOCz2WwkFyTz0eGP2JC1ARuNy018XH0YHzyeYH0wxnojRrORanM19bZ6JodOZkHcAmK8Y7p9TO5ad+bHzmd+7HxOVpzkj+v+SJYhi8WrFvPMrGeYFTmr288pRJ9Xa4D3FkLBAeVnlQYGz4HEhbDtVSg9AR+eD7Puh1n3gaaVt06rFXa9C+ufUAJ6AP9BMPMv4OoN6/8Jxanw06Pw6xtw/kuQuOCMPMV+Z9f7yteRl3fbIc0WM7sLdgMwqaHbizP6fpp9z1Syt7Ovmz8mwbwQQvS6TgXzp659V6lUba6F1+l0REZGctlll/H44487P0LRKTqNjkD3QIpqisjXagioync8NizM2xHMz0qQYL6n1FvrWZO+hg8PfUhKaYrj/unh05kVOYsJoRMY4jsEtcrpZhLdYpDPIJYtWsbSX5ayM38nf1r/J/4y/i8sHr5Y1tGL3w6rBb64RQnk9YEwc6kSQHqFKI+PvgpW3gf7lsEv/4K0jXDWAxA7E9Sn/BsuPgbf/gkytyk/BybCrHthxKWNwf/Q82D/Cvj5SajIghXXwzWfQvw5Z/Y593U5uyFvL2hcYOx13XbYgyUHqamvwU/tSrzZ3PWZ+T6YZm+z2Ror2fdQMC8V7YUQou/oVDRhtVodN5vNxo033tjkvlNvtbW1nDhxgmeeecbpNfOvvvoqsbGxuLm5MXnyZHbs2NHqth988AEqlarJzc3Nzanz9nf2dfP5Wi0YGmcehoUpb8CHZd18jzBbzXx17Csu/PpCHtj0ACmlKbhqXLki4Qq+ufgb3jjnDa4ddi0Jfgm9Hsjb+br58ua8N7ks/jKsNivP7nqWu36+i4Jq52ashOgTzDVKAbWOWPMQHFsDWje49jOYemdjIA/g6gmXvK6k2bt4QuZW+OhCeGkkrH0U8g/Cpufh9elKIO/iCYuegzu2wegrm87iqzUw9lr4UzKMuASsZiWgT9/cvc+/v0tumJUffhF4BHTbYX/NU9bLT/SMUT78GPLb3L41fXlmvqiqljKjGZUKhnRzJXs7ezCfVVpDdW33dCrKq8ojuSCZlJIUMiszKa4pxlRv6pZjCyHEQOb0mvlHH32UpKSk7hxLEytWrGDp0qW88cYbTJ48mZdeeon58+eTmppKcHBwi/t4e3uTmprq+Pm3OrsY6hHKgeID5Gs1YKxSUkhdvRyFaqQIXveqtdTy3YnveOfAO+RU5QDg5+rHdcOu48rEK/Fz8+vlEbZNp9Hx6NRHGew7mBeSX+DnrJ/Zmb+Te8bfw+UJl/eZCw9CtMlsgpRvlfTszK3KzPiYq5SZdZ/IlvfZ8Tb8+rry/SVvQOT41o8/+kqlRdqWl+HQV0qBti0vKTe7IfPg/BfBt506MVpXuOQtqDMqFxKWXQVLvm37/L8Vpko48D/l+/E3deuhd+Yr1fEnBYwEfnJ6Zj7QPRBQZuZtNluf+qxxNF+pZB8b4NFjbeP8PVwI9HSluKqWY4VVjI3ydeo4dZY61meu54tjXzgutJxKrVLz0OSHuDLxyi6OWAghBq4uBfM96YUXXuB3v/sdN92kvJm/8cYb/PDDD7z33ns88MADLe6jUqkIDQ3t0XH1B/YieHkueqBKmZ139XJUtD9RVC39YbvAVG9if9F+dhXsYlfBLvYX7afWovT09Xfz5+aRN3NFwhXodf2nuJVKpWLx8MVMCZvCY1sfY3/xfp7Y/gQ/nPyBR6c+yiDfQb09RCFaVnwMdn8Ie/7btNhccSqs+wesewJiZ8CwC8E/DnyiwDcKMrbBqr8q2855WJkpb4//ILjgZVjwNBxdraTMH/sRXL1gwb+UCwcdDey0LnDlh/DfKyB9E3xyKdy0UqmeD0orO3ONcuw+FCz2uAOfgblauRgTM63bDmuqN7G3cC8Ak8KmKHcanFwz35BmX2uppcpchZdLz6SzO6MnK9mfKjHUk+LjtRzNN3QqmLfZbBwtO8q3J77luxPfUVZbBoAKFZFekdTW11JdX43RrLRd3Zi9UYJ5IYRoQ4+3pnNGXV0dycnJPPjgg4771Go18+bNY9u2ba3uV1VVRUxMDFarlXHjxvHkk08yYsSIFretra2ltrbW8XNl5cCZrXak2bs1BJNV+RA4hFBvN3z1OsqNZo4VVDEqUtojdVROVQ4bszfyS/Yv7MzbSZ21rsnjIfoQbhhxA5cnXI671r2XRtl18X7xfLTwI5anLufl3S+zu3A3l357KecNOo/fjfodsT6xvT1E8Vtns0HhYTj8rTITX3i48THvCBh/oxKYZ25XKtNnbFaC5fRNpx1IBdhgzLVKgbrO0LnBiIuVW101aFxbL4rX5nHc4Zrl8PHFkL0T3jpbSfc3G5UUfAC/WOU5jb0ePAd4rRObrbHw3YSbuvUixr6ifdRZ6wh2DyY2pCGrsLoQLPWdfu3cte7otXqM9UZKakr6VDB/rIfXy9slhHix5XiJY31+e9Ir0lmVtopV6atIq0hz3B+sD+aSIZdwSfwlRHhGOO5fl7mOu3++mzJTWbePXQghBpIOv4PNmTMHlUrFhx9+SGRkJHPmzOnwSVQqFevWrevw9sXFxVgsFkJCQprcHxISwpEjR1rcJzExkffee4/Ro0dTUVHBc889x7Rp0zh06BCRkc1TLJ966qkBW5jPMTOvbXh5G9YFqlQqhod5s/VECSl5lRLMt+No2VFWp63m56yfOV5+vMljQe5BTAidwISQCUwInUCcd1yfSrXsCo1aw3XDruPsqLN5asdTbMjawLcnvuX7k9+zIHYBt42+jcG+g3t7mE1ZrVB6UimalbtHCfD84pTCYrEzlXXPom+rKYPCI1BdBMZiqC4BY4myTMhsbLyVZ0FZYzCAWguD5yrB35BzGgOzwHgYtxjKMpTZ3uxkpehceSbUVgI25W/jgpe7FjS6eHTpaePqCdd9Dh9dBHn7wFLb9PGydPjpMVj/fzD8QphwM8RMH5iz9dm7oOCgckFjzNXdemh7GveksEmoPIOUjgU2i/L35t1y68+2BLgHYDQYKTGV9KmLnPbgOr6Hg3l7pfwjBRWkV6RzuOQwKaUpypp3Q2aT7i0Wq4WimiLHzy5qF2ZFzuLiIRczPWI6WnXzj6L+bv4AlJpKmz0mhBCiUYeD+Q0bNqBSqTAajY6fO+pMBDlTp05l6tSpjp+nTZvGsGHDePPNN3niiSeabf/ggw+ydOlSx8+VlZVERUX1+DjPBMfMvLrhzdTQtKL91hMlUgSvFZmVmaxKW8Xq9NVNAniNSsPY4LHMjpzNrMhZDPIZNGCC99aEe4bzypxXOFh8kDf3vcmG7A2sTFvJqrRVXJl4JUvHL+25pQS1Bkj5Dg7+D+prIXqKEsBETVKCp/payEmGjC2QvkX5vraFv+ld7yoVsaOnQuIiZYZT10ZhTJtNWQtdlKrcio+CRxAkXafMkA4ENpvyvNI3gz5ASWX2bLkOSTNWq7LOuLpICbTtN40OAhMgaKjy++rIv42qImVte8ZW5TUsOAh0sGidxhWGzFVS5xMXgHsbdSn8YpR2cqeqKVfakvnFKenuvc3dD373MxQdUZ6bzr2ht7oKjnwPu95T/sYP/k+5RUxQnlPC/IEV1O96T/k64tK2X1MnONbLh05SChF6hoAhV/l7diaYdwsgy5DVp4rg2Ww2jhcqa+bje6j4HUC5qZwituEW/h371Ee54OuadvfRqrRMDZ/KwriFnB11Np4ubY/Pz1V5/SWYF0KItnU4mP/5558BiI6ObvJzTwgMDESj0VBQ0HQ9W0FBQYfXxOt0OpKSkjh+/HiLj7u6uuLq6trlsfZFIXolo6HYZsYCaE5rTwdS0f5UNfU1/Jj+I18e+5Ldhbsd9+vUOmZGzOTc2HOZETEDH9ffZibDyMCRvDL3FVJKUnhz/5usy1zHitQVbMvdxpMzn2RM0JjuOZG5RgnO961QAhizsfGx9E3As8osbGAClJxoPoOpdYPQURCeBMHDoOAQHFsL5RmQ9oty2/kOXPQf5eLAqQz5sPFZ2P9ZyxcFNj6rFDebcDPEn9tyWq49SD62Vnkep44fQOcBYaOV8YUndTyAtpghd6+SLp6xVbnIEDlRmaUdMq9jM8M15cqYjq2F4+ugIrPp4wHxEDsdwscpxdlOfU72ixvFqcr69NOf1+nc/ZT1zkGn3AITQaVWqr2nNzyP4tTm+/pEg1eocpHBI0BpFefmrfzudO6g04ObD8RMVdaSO8vdV7n1JWpN43r5UyVdr9zy9inB7r4VkLMLPr1K+XufdR8MvaBpm7z+qKYMDn2pfD/h5m49dLW5moPFBwFlZh5QOhYYcp3uNW8vgldcU9wtY+wORYZaKk31qFUQF9jFjJFT1FpqHXVituVuY1/RPqw2K7qGt0QXtStDAxIZ5j+MEQEjGOQ7qNlse6RnZKfeQ+2FY431RmottbhqBubnNSGE6KoOB/OzZ89u8+fu5OLiwvjx41m3bh0XX3wxoLTFW7duHX/84x87dAyLxcKBAwdYtGhRj42zr/J380etUmO1WSnVqAk6pcjP8LDGivZ9rQrvmWSqN5FSmsIPJ39g5cmVGMxKaqJGpWFK2BQWxC1gTvQcvF28e3mkfcewgGG8dPZLbM3dysNbHibTkMmSVUu4ZeQt/GHMH9BpdB0/mNUC+QeU2cbcPUp6fGEKWE9pc+Q/WEm19QxunL2tzG5cI+0RrMwqx0xXgvPgYcoM8alsNig5rhQp2/IylByD9xbApNtg7iPKuuTNL8Gvb0J9w+ySWqucOyhRSdXO3QMn1sPxtcrNM1R5zCNQCTY9AqEyt+Ug+XSpPzR+7xUOQQkNBdmila96f+VYFVlKOnl5JuTvbx5Al2fAwS9A6w7x8yBmhhLUu+iVgFetVWZ5c/cot9KTTffXuCi/M2OpMiNecky5JX/Q/mun1jY+b72/8r3ZqJyvLEMJyrK2K7f2BI9oeA0bbl5SwLRVYWOUJQFnPwTb/gM73lH+DX22pGGD0/4vt1/80OmVvwuvUIieply0iZjQdobK6Sz1ytKGoiPK32SdUSlSZ65RXvs6Y8P3DfdZ6sDNt+GiTMO/E89gpfCgb4zSXcB+0ajOqCyr2PMJ1JsgZKTSNaAb7S7YTb2tngjPiMZ12V5hwJ4B1WvePisf5a9vtcCtzWajwFhAZmUmQfogIj0jm/zfbX88pSSFgyUHSS5I5kDRgWZ1YhL8EkjPiqGseAhvL7mcKXEdvDjZQd4u3mhVWupt9ZSZyhwZh0IIIZrqkwXwAJYuXcoNN9zAhAkTmDRpEi+99BLV1dWO6vZLliwhIiKCp556CoB//OMfTJkyhSFDhlBeXs6zzz5LRkYGt956a28+jV6hUWsIcAugqKaIQo2GoFNm5ocEe6LTqDCY6skpryHSr49XXDeblA+Lev/2t7XZoOAgtkPfUHDiR0rcvTHGTMEYNgajVktJTQkppSkcLjlMWkUaFpvFsWuEZwSXxV/GRUMuIljfvR9KBppp4dP48sIveWrHU/xw8gfePvA2G7M3cv/E+xtnvU536uxy+hbI+rXlGXCPYGXGecw1EDG+MYV4/I3K1/JMpa93YAIEDG4/xVilUgLywHilv/ePf1eChh1vwpEfoM4Apgpl28hJMOch5eLA6RcFSk40VEz/RCkoecq/qSY0rkqwNHiuksZ7KmNJ45r+otSGFN/ctsdv5+7feOEiMAFO/qwUfyvPVJYjpHzX/jH8Bynjij9Hqe5un9E3lirF4jK2KBdUTk919whqmGEfqsyw+8W2XjDMXKPM3hcfVQI/+3KF0hNgs0LoaOXcMdOUpQ8d+XctmvIMhnP+AdPvhu2vKRei7DUATmWvMWBXdAROblC+17goAX3Y6MblEUGJ4OKpXPwqTm187YpSlfvsBfm6i/0i0OkXqrq58B3AjvwdAEwOm9x4p/3C0QDqNX+8qHmKfU19DTvydrCvaB+HSw+TUpLSJHVdo9IQ4RlBjHcMVqzNHrcLdA9kQsgEJoZOZGbETMI8w7jp/R38XFPE8YIapsR173NRqVT4uvlSXFNMqalUgnkhhGhFnw3mr7rqKoqKinjkkUfIz89n7NixrF692lEULzMzE/UpaYVlZWX87ne/Iz8/Hz8/P8aPH8/WrVsZPnx4bz2FXhWkD6KopohijaZJ+x0XrZrBQZ4cyTdwOLeybwbzVmtDuvVyOPyNEnD5REH4WCU9OWyMklJdp3wQrKgpYXvuVlLydnLYZiTFxYVyVw1YyyAtA9JWtHiaALcAJoZO5NL4S5kcNln6qXeCj6sP/5r5L86OOpsntj9Balkqt/x4C9MjpnPPuHtI9E9smK3+SUntPvkL1FY0PYirtxKwR4xrTD33jmj7g7xvdPs9vFvj7gcXvQojL4fv/qwEwqDMDs99pO31xwGDlQDq7IeUVHFDgTKbaCyB6mJl9nPwnIYguQP/pmqrlFnVsjRlBr4iU/laUwbe4Y3t03yiGgKtoU3TqOPnwbn/VFKvU75TZtXNNY0zpfW1Shs2++81bGzrgbPeH4YuUm5dpXNXAsSw0U3vt5iVW0d+N6JVNpuNels9OrVOed3m/F1Js68pP21DqzLLbW6YMa+rVi6wZGxV/m+tKlDqFWRu7fjJdR7KRTH/QcoSBxf70gf3pssgXPSg1il/y8aShmKGxcoMeHmWknVibpiNt9O4KMF9yAgY3b2F7+CU4nehp1xs9GpYJz+AZuaPFVQBNkIDalh+ZDkbszeyI3+Ho3WqnT2AL6opoqa+hkxDJpmGzCaPD/IdxDD/YSQFJzEhZAIx3jHNMvkSQr34ObWowxXtO8vPzY/immKpaC+EEG3ocDD/j3/8w+mTqFQqHn744U7v98c//rHVtPrTC/C9+OKLvPjii84Mb0AKdg/mMIcp1GqazSIOD/fmSL6BlDwD547oI1e7q4sbZ24PfKF84DtVRcOHwFNmINO1Wj7x8eJbTw9q1GpwA1DawmlR469xw8NsQm+uwcNqw8tqJcE1gOHT72NY1CyC9cG/2WUG3WV+7HwmhEzgzf1v8nnqZ2zJ2cLWnC1cYNZyW34mMfWnpM27+ykzyzHTldnrkJHKOuEzbfDZ8Idtyvp53ygYfknH1xtrXWHQWV0fg6unsu47Zmr727ZGpWq4wDW26+PpaRpd82wH0SKj2ciewj3sKtjF7oLdlJpKqTZXU22upqa+Bhs2Qj1CifGOIdY7lhjvGEYFjmJ00Oi2L0gOmg2TfqdkMJWeVLIxilIaZ9/LMwGbUpMgaOgpM/ZDleUg3pHdsy7fZlOyQQx5ygUBfYBycaAH/i82W8w8vfNpUkpTUKFqGszbM2ec7TXfMDNfWtM9Bdq25Gxhe952AtwCCNQHEuQehL+bP0U1RWRUZpBZmUl6ZTqlplLcte546Dzw0Hqg1+mpMldRZCziYHkWnonlfFNi5ptTrjFEuAUy2TeB4f7DGR46jviQcbi5eGCz2RzHT69Mx2azMcx/GPF+8bhp21+GkRDcUNE+v2eCeX9XqWgvhBDt6XAw/9hjj6FSqbDZOlhtGBzbOxvMC+cF6ZV+xEUaLRgqlNkZnRLoDg/z5ktySOntInipq2Hvf5UgvmGtsa3hhqtPQ7r11ZiDEqjOScaYt5vqggMUlBzjc5d6ftFYsTV8/hukdmec/3CGx85jeEgS8X7xuGgaqlSXpikpyZteAFMqfHMfXPaOUjxMOM9qhaIjBGRs4W9ph7g+r5R/e2pZ4+nBt7p6vo0KZ5JVx2XBk5g78gZcoyb1TvDeEldPmHF3b49CCCxWCwdLDvJL1i9sz9vO4ZLDTZYAtSS/Op/86nzHjDMoLUkXxC1gYexChvoPbf1CpUqlZJoEnNZess6ozOB7BPZshXyVSilu6BHQc+cACqoL+Msvf2Ff0T5UqLhn/D2O90Wg22bmu6MA3ncnvuNvm//W5eOgUn69amAsrsyqqmZ2eRGDzZmo2N10Q30AKu9wgn2jCfaJYqJvtLKEoyQLDCVKloXGBaoKGzOHKrKUiz0LnwGNjhERSk2ZgzkV1FusaDXdm91mb08nM/NCCNG6Dgfzjz76aE+OQ3Qz+4eWQl1DQGvIV9Ju6SMV7QsOwadXYVCp2OPmyi4/X3Z5+XBYY8PxMbb8F/jll+b7npKpe1bkWSwevpiJoRNb//DqHwfT74LhFyvFovL2wieXw1kPwKz7G2ebLOZTijkZG1NU9QHNP/j+VtUaYM9/IW2jkqJb0/ghKxp4ThXIjcGjeE1rYnPlcXaozewo3oLPtoOcl38e08KnkRSSJIUFxW9KWkUaqWVNq/fX1teyI38Hm7I3UVbbNFiJ8IxgfMh4JoRMINo7Gg+dB3qt3tEKMtuQTUZlBhmVGZysOMm23G3kVefx/sH3ef/g+8R6x7IwbiEL4hYwyGdQxwbpoh8wyyB25e/i3l/upcRUgpeLF/+a+S9mRc5qulFX18yfkmbflWKyG7M38vAWZbJjduRsPF08KTYWU1RTRImphAC3AEcWRrR3NEHuQdRYaqgx11BtrsZorkZfU45v9gH0xzYzxGIgyGLB/dSJF+9IpeChsaTh/2xbwzKhYqXAZmcNuxAGn01CsBdebloMpnqO5BsYGdG9HV/sFe1P//chhBCikQTzA1Swu1LErchFmY2nqqBZMJ9ZasRgMuPl1rPpr7WWWoqMRY4KuumV6WSkfEV6RChpLi5YHVu2nfXhrnVHr9XjofNgavhUrh92PbE+sR0fiF8M3LwGVj8Aye/Dhqfg1zeUGWZzddNK6qcLG6sUZRt5GXgGtb7dQHb8J/ju7qZLIHR6pfd7zAyl73fYWEaq1bwG5Fbl8tXxr/jq2FcUGAtYdmQZy44sQ4WKof5DGR8ynljvWPQ65TX10Hng6eLZ6RZGQvRVqaWpvLn/TdZmrG1zOy+dF9MipjEzYiaTQicR5tl23/NA90DGBo91/GyqN7EpZxOr0laxMXsj6ZXpvL7vdV7f9zqJfoksjFvIlPApeOm80Ov06LV63LXuA2qZkdlqZkfeDlamrWTlyZXU2+qJ94vn5bNeJso7qvkO9pn56iKlWn9rRR1bYU+zr7XUUm2ubrdvekuSC5JZumEpFpuF8wedz//N+L+2l0pk7YDUlcqYq0uU4NyQ1+T/5FJ8cJ94pVKLJChRWS5xahtHSz3UlDbMuGc3dM7IVG7Gksa6G2ajUoDWI6ixfseJdUoxxEqlcKdarWJ8jB8bUovYmV7aY8G8pNkLIUTr+mwBPNE1jjR7XcNLfMrsg7+HC6HebuRXmjiSb2BibOsVpYtrijlcclgJpHV6PLQeuGvdMdQZKKwppLimmCJjEWWmMqrrqxtmCoxUm6spM5VRVFNEZV0rGQAuStZAtFc0E0InMCFkAmODx+Kla9o/WqPWoNfq0XRHirbODS54SWnL9d3dTWaWHVRqpaCTi14ptFeZo8zm5+2FNX9T0vMn/U75OoA+DLfKWKo8732fKj/7Rit9oGNmNBQjdGlxt3DPcO4ceye3j76dLblbWJ+5nl0Fu8iozCClNIWU0pRWT+nn6keMdwwx3jEk+CUwPnQ8Q/2GdvhvwGg2dkuwYqo3UWetQ6/VN+ubDEqKdHV9NcXGYuUiVcNsabYhG/Np1b9dNa5Ee0c7nleMdwxhHmGNy0FaUG2uJtuQ3eTYRcYiEvwSmBA6gXEh4yTLoQ9KKUnhjX1vsD5rveO+0YGjm7zWapWaYf7DmB01m7HBY5Widk5y07pxTsw5nBNzDtXman7O+plVaavYmrOV1LJUJStgd9N9NCoN0yOms2T4EiaFTuqXgb3ZamZPwR7WpK9hbcbaJjO4C+MW8tjUxxzZDM3oA5Q2i9Z6qC5UCk92gl6nXBCpqa+hxFTS6WA+tTSVP637E7WWWmZHzuYf0//ReiBfVQQ/PaosS2uJxpWM4LN5LGMUtkFn88F501s/sUarpNN7BkPoyE6Nma/vVIL5U5YmTGgI5ndllHHT9O4taW9Ps5dgXgghWtdtwbzNZqOkREk3CwgIaFJpXpx5Qe4Nafb2z2dVTYv8DAvzIr/SREpeZavBvKnexPUrryenKqfL43FRuxCkDyLGO4bokkxic/cTEzSKhMs+6p1WcGOuhvhzlUBdp29alVmjaxqkVxfDwS+VYDZ3Nxxbo9zCk5RK0gkLu6cwVF9jroFDX8HaR5SZIFQw5Q9KRXfXjn9w1ag1zIqc5UhzLTQWklyQTHJBMkXGIoz1RoxmI8Z6I+W15Ur14toyyorK2Fu013EcT50nScFJjAsZ1+yCT0VdhSPYzajMoLy2nAjPCCXVOHYBCX4JLQYrpnqT0vWhpphCYyEF1QVkGjIdwXN+deNFMDeNG3qdHjeNGyaLCaPZiMli6tSvdFvetmb3+bj6EOQeRKB7IF4uXpTUlCgXyRoqTbd2nA8Pf4gKFYn+iYwIGEGwPphAd6VwVoB7ABabpcnFtcq6SoqMRY7nW1JTwiDfQSyMW8jMiJkdKnglWmeqN7E2Yy1fHP2C3YVK5KxCxfzY+dw2+jbi/eLPyDg8dB6cP+h8zh90PhW1FfyU8ROr0leRVp6GsV75W7Bhw2KzsDF7IxuzN5Lol8ji4YtZGLewzYtLPcFUbyLTkElGZQYWm4XJoZMdM7ItKTOVsTlnMxuzN7IlZwsGc2PxNX83f86NOZeFcQtJCk5q+wKFWq0UwavMUYLTTgbzoMzOZ1dlU1JTQox3TIf3y6vK4/drf4/BbGBc8Diem/1cyxdzrBZI/gDWPd7YQnPk5RA8VKn+rw9QahyEjOCjn3L4OS2Nm0J8O/08OqyFpQkTGj4/7Eov7dJyg5Y40uxlzbwQQrSqy8H82rVref7559m8eTM1NcoHTzc3N2bOnMk999zD/PnzuzxI0Xn2mflS6qkHtIbmFe1/Ti1qswjesiPLyKnKwUPnQbA+WKmmbK7BWG/E08XTEYDYgwf7uk4PnVJh19fV1xFgeLt4K2/yhnx4aRRY6uDSh6E3e7rr/TvW59ojECbfptyKjiofrpLfV/qFL79WqcqetFhpBWUsVtIfa0qVns329ETfGAgYpLR16susVmUt/L5P4fC3jb3gAxPhov8oKfVdFKwPZmHcQhbGLWzx8WpzNZmVyof7tMo0DhQdYE/hHqrMVWzK2cSmnE0dOk9OVQ7vHHiHdw68wyCfQUwOm0xlXaVjPWqRsahJINAek8XUavCu1+obq4v7xBDtFd0sOK6qq3IELfbq1CaLiYraCipqKzhefrzFY3u7eBPrE+uoXO7v5s/B4oOOLIcjpUc4Unqkw8/jVCcqTrA2Yy0eOg/mRM1hdtRs9NrGmUwbNgx1BsdFgKKaIspN5VhPWRwDyoWWU6urR3tHE+AW0OIHe6vNSkF1AVmGrGYts7RqLQHuAQS5B+Hr6ttuYGCz2aipr6GitkLJFGp4bctqy/B28SbIPYggvfL/VIBbQJvZGnWWOuXCxykZRsZ6IxZr28XorDYrm3M288PJHxx/T2qVmoVxC7lt1G0M8u29f/M+rj5clnAZlyVc5rjP/jvLrcplReoKvjnxDallqfx9y995MflFLhpyEZfFX0a0d/MWkDabjbLassa/B6OyrttV4+p4/cM9w1vMYrHvn1aRxi/ZSrG/tIo08qqbFqBTq9SMDhzN7KjZzIiYQU19DYdLDjtuJytOYrU1/v35ufoxO2o2C+MWMil0UqvnbpFXaEMw79y6+UD3QLKrsjtdBG956nJKTCUk+CXwytxXWr6QZiyF/14BObuUn0NHwXkvQtTEFo95vFCpyTAkuPPp/h3mCOYbX7Mxkb5o1SoKKmvJLqshyr/76i74uUowL4QQ7elSMH/ffffxwgsvNKtwX1NTw48//sjatWu5++67ef7557s0SNF5/m7+aFQaLDYLJRoNIc1m5huK4OW2HMyXm8p5Z/87APxt8t+4cPCF3TOwba8qgXzUFIiZ1j3HPJOCEmDBkzBzqfJcdrwNBQdh9V87tn/0NJh6JyQu7P3K7qUnIW9/w5rJhkrF+Qearon3jYZxN8C0Pylt2c4AD50HwwKGMSxgmOM+i9XCkbIj7MrfxaGSQ9SfVt/AXevuSF2P9Y4lWB/Mr/m/sjptNZuyN3Gy4iQnK062eD5XjavjolSwPtiRCm8PTD10Hk2CPFO9CTetW5OLV87MZtpsNipqKxxBcpGxiCpzFf5u/o4gNMg9qMU04csTLgegyFhEckEyaRVpTYLZUlMpOrXOsT5ar9Pj5eKlHNc9iEB9ID4uPuzM38mq9FXkV+fz3cnv+O7kd83O5SydWuf4vQa6B6JWqckwKBcxTg/iW6JVawl0D2xycQGU4NkeaBvrjU0Cu/aoUDnaeul1euosdUrgXm9s9jfljAjPCC4ZcgkXD7mYEI+QLh+vJ6hUKvQ6PUP8hvDQlIf4Y9If+eLoFyw7soxCYyHvHXyP9w6+x6TQSVwafykuGhcOlxwmpURZGtNeyrNWpSXSK9JxIdf+Na86j1+yfiG7KrvZPl4uXsR5x2GymDhadpS9RXvZW7SXl3e/3OI5Ev0SHdk+owJHOb8Eq5d6zR8uOQzAtUOvbX2ZzK9vKoG8qzfM+TtMuKXNdf3HC6sAiA/2anWbLnP8vhovfri7aBgZ4cPerHJ2ZZR2azAv1eyFEKJ9Tgfzn3zyCc8//zzu7u7ccccd3HDDDcTFKeul0tPT+fDDD3nttdd46aWXSEpK4vrrr++2QYv2qVVqAt0DKTAWUKTREHLahxV7MH8k34DZYkV3WkuZN/e/icFsINEvkfPizuueQdWUwa73lO9nLu3f6809AmHeozD9z0pAn5Os9FHXBzTc/JW0SHuQXJ4FxanKrHfmVvCLgyl3QNJ1Sq/lM6nkBPz8JBz8Hy0WHXT1hhEXKwX/oqb0iSUEGrWGEQEjGBEwosP7LIhdwILYBRjqDKzPXM/x8uP4u/krAWZDoBykD8JL59XuDLCLxgVffLv4LJpSqVT4uvni6+brdAp2kD6IBXELnB7DzMiZ3D3+bvYX7Wdl2koOFB1ocdbdHpQH6YMcFwrtbNgory1vsswhtyoXs9VMXnVes5lXUAL1SM/IZhcq6ix1lNSUUFZbRr21vskyh7Zo1drGCxXugfi5+TVbVlBrqcWGzXERgJZXMDiWU9gvgnRkpjfaK5pLhlzClPApbRcw64N8XH24ZdQtLBmxhF+yfuGLY1+wNWcrO/J3sCN/R4v7OP4dNfy+jfVG0ivTHRdq0ivTSa9Mb3FfnVrHpNBJzIycyYiAEcR4xzTJwsivznek//+a9yveLt4MDxjOsIBhDA8YzvCA4d23NKubes2X1HQ8mLfZbI5MmqEBQ1vbCPavUL5f9ByMuarNY1bX1pNTrvxB9+zMfPNgHpR183uzytmVXsYlSZHddjp7mr3BbMBsMaPT9GyxXiGE6I+cDuZfeeUVNBoNq1evZubMmU0eGzFiBM888wwXXnghZ511Fv/5z38kmO8FwfpgCowFFGo1zT6sxAZ4EODhQkl1HT8fKeTcEaGOx7IMWSxPXQ7A0glLu6fwHChBb12VkpYef273HLO3ufvB7Ps7tm1lLux4S7mgUZYGq+6DLS/DZW+fmSyFyjz45WnY83Fj5f6I8coSAPtyAL84iJ2u1A8YILxcvLhoyEW9PYw+S61SMzZ4bJPq6F1ltpgd6/7tWQcWm4Vor2hivWMJ8wxrM0g+df+WZvHtxTjtXRDaK3ZoTy0/tT6D0WzERePiCNrtx+lUmvYAolPrmBczj3kx88iryuOr41+xOn017lp3hvk3BtJDfIe0Wl/BvoQi05CpXEQxFjsyRjxcPJgRMYOpYVNbL0oHhHqEcmXilVyZeGW3r8Fuphdm5guMBZTXlqNVaYn3beUiXtYO5T1C5wHDzm/3mCeLqpXxeLjg79GDNQ/safZV+cqSrIYLvRNi/Xlncxq70rt3Bt3H1Qe1So3VZqWstqx36usIIUQf5/SnloMHDzJjxoxmgfyp7I/v3LnT2dOILrAXwSvSaJQ331No1CouGx/JWxtPsnxnVpNg/t+7/029tZ5p4dOYFt5NQWZdNWx/Xfl+xj39e1beWd7hMO8xmHmvsiZ967+VdkAfnAezH4BZ93Z/6n11MRxfB8fXQsp3yrp+gCHnwNyHlWr0QnQznUZHmGdYuy3Wemr/09lTy/U6PQyc61Q9JswzjDvG3sEdY+/o1H5qlbrbX7ce1dVe807MzKeUKF08BvkOan15zn7lYjrDLuhQ5taxQqVWw+CenJUHpQI+KuVisLHE0aZ1fIwyg3600ECF0YyPvntm0NUqNb6uvpSaSikzSTAvhBAtcTqYd3NzIzy8/eqv4eHhuLic2eq4QmEvgleo0YChBOrrmrQRu2piFG9tPMmG1ELyKmoI83HnQNEBVqevRoWKpeOXdt9gdn+sFIXzi4PhF3ffcfsjV0+ltd2Yq2Hl/bBvGWx4EtI2wqVvgU9E2/vbbEphOmNJQ6/hYqgpb+gLXKPcTOWQsVUp0ndqKn3UFGV5QH+sVyCEEN2pq8G8e+eDeUeKvX8rKfb1dUr3FGg3vd6ucb18DwfzGp3Sd766UMlmaAjmg7xciQv0IK24mt2ZZZw9tPuCbn83f0pNpZ2uSyCEEL8VTgfz48ePZ//+/e1ut3//fiZMmODsaUQXOGbm7b3mqwvBp3E92+AgTybF+bMjrZTPdmbz57lDeD5ZKVZ4weALSPRP7L7B2Nf/Tb2zzSI+vymuXnDJ6zD4bPj+HsjYDK9PVZYh6NwbWubpwWpuGrgbS5Qigh0VMgri50H8fIie8tvMihBCiNOdmjbuhED3QIBOVbNPKVVm5of5D2t5g2M/KhdjvcIgbnaHjnmsIZjv0fXydl6hDcF8PoSNdtw9PsaPtOJqdmWUdmswL+3phBCibU5HVQ899BBz587lmWee4f77W14z/Oyzz5KSksIrr7zi9ACF8+wpaYUueqBEWTfv07Q4zTWTopRgflcWSYn5JBck46J24U9Jf+q+gVTmKf3ZAYZ1U1X8gWT0lcra9S9uhry9kLGlY/vp9EqvYY8AcPNV0jF1+sYLASEjYMg88O6elFchhBhQ7Gvmq4vAYlZmnjsh1EO5GFBgLKDeWt+hegvtzszbU+xHXd7hZVcnzmgwHwb5+5vVGZgY68cXydndvm5e2tMJIUTbnA7mVSoVf/zjH3nwwQf5/PPPWbx4saOafVpaGp988gnJycn8+c9/Rq1Ws3Hjxib7z5o1q2sjF+2yp9kXaxte5hZmHxaODOPRbw6RU17D54d/AeCiIRc5PqR0i6OrlK8RE8Crb7Zr6nUBg+HWn5RAvqZMSZWvq1a+qtRK9Xx74K4PVCrmu3RfCyAhhPjNcfcHtU7JfqpqfrG7PcH6YHRqHWarmfzqfCK92t6/3FTu6O7QYjBfUwZH1yjfj+5Yin1tvYWMUiPQw23p7FpZmjA+RmkjtzernLp6Ky7a7unqYJ+Zb68lohBC/FY5HcyfddZZqFQqbDYbycnJ7N69u8nj9t7z//73v/n3v//d5DGVSkV9fdd7+oq2OdLs7e+pLVTsddNpuHRcJB9sTWd3/kGAbq1qDcCRlcrXoYu697gDjUYHg87q7VEIIcRvg1qttKerzFaC004G82qVmgjPCNIr08muym43mD9SpszKR3lF4enSwiz6oa+VJVTBIyB0VIfGkF5sxGK14emqJcTbtVPjd0orHQAGB3ngp9dRZjRzKLeCpGi/bjmdo9d8rczMCyFES5wO5mfNmtXzlWZFl9jT7EuxYAZ0rfTSvXpSFB9sPUmFJQOVpo21fM6oNUCaMuNPYjf1qxdCCCG6g1doYzDvhEivSNIr08kx5EA7K5qOlLSXYt9QW6aDhe+gsfjdkGDPM/OZrJWZeZVKxfgYf35KKWBXelm3BfOyZl4IIdrmdDC/YcOGbhyG6Am+rr5o1VrqrfUUazSEtVLkZ2ioN8OjzWRpatGoXIjzieu+QRxfp8w0+A+CoG4sqCe6Xbmxjp3pZQwL8yLST1L4hRC/AY7g1Lle8xGeSveR7Krsdrdts/hdWTpkbgNUMOqKDp/f3pbujKyXh1Zn5kFZN/9TSgG7Mkr5HYO65XSOmXkJ5oUQokVSVnwAU6lUBLsHk1udS6FWQ1grM/MAExKMZOWAqi4Mjaobe52nNqyXT1wkVdT7sFUH8nj4m4MUVylV8sdE+bJoZCiLRoUR5S+BvRBigHIEp87NzEd5RQGQbWg/mG+z+N3+z5WvcbPAu/22v3ZnrC2dnaMDQPPPExNilVn0Xell2Gy2bskUsAfzsmZeCCFa1j0VSkSfFahXWucUaTRttt/x9FbemI1VoWw/2U1vmpZ6ONZQzGeopNj3RSVVtdy5bDd/+O9uiqvqCPR0RaWCfVnlPLXqCDOf+Zkr3tjKznT5ICWEGIDsRVmdTbP3VNbJtxfM19TXkF6ZDsCwgNNm5m22U1Lsr+7U+Y+fyUr20Hjxo6oArJYmD42M8MFFq6akuo70EmO3nM5ezV6CeSGEaFmXZ+ZNJhO7du0iNzcXk8nU6nZLlizp6qmEE4LdG9rTaTRKa7pWHC9PBcBqCufTHZlMHRzQ9ZNnblOq87r7Q+Skrh9PdJv8ChO/HC3k6dWplFbXoVGr+MPswfxp7hAqasysOVTAqgN5bD9Zws70Mq54YxvzR4Tw1wVDGRR0hj40CiFET3MEp86vmQfIqcppc7ujZUex2qwEugc6+tM7FB+DkmOgcYGh53f43BarjZPF1cAZDOY9ApUOKzar0tLPq7HzjatWw9hIX3akl7LxaBFxgR5dPp19zXxlXSVmqxmdunPtA4UQYqDrUjD/7LPP8uSTT1JZWdnuthLM947G9nQaKC9UrqSf1rvWZrM50v8spnBWHcyjuGo4gZ5drIyb2lDFPmEBaGRFR085Xmggp9zEpFh/3F1aXiJxvNDAhtQi9mSWsyezjNyKxgtvQ0O9ePbyMYyK9AEg2EvD4ikxLJ4SQ0GliZd+OsaKnZmsOVTAupRCrpsczWXjIxkW5o1OI8k9Qoh+rJWCbh1lXzNfVltGVV1Vy1Xqaaf4nf29MnYmuHl3+NxZpUbq6q24atVnrs6JWqN0ADDkKTevpm1sF4wMZUd6KZ/uyGTJ1Jgup9r7uvqiQoUNGxW1Fc0vhAghxG+c0xHWf/7zH/76178CMGrUKOLj4/HyOgM9TkWn2CvaF2q0LV5JBygwFlBWW4ZGpWFE0FAOZFWzYmcWd549xPkT22xw5Afle2lJ1+2sVhu/HC3i3c1pbD5eDIC7TsPZQ4NYNCqMOUODyS6r4Yf9eaw6mMfRgqom+6tVSuHDRaNCuW3W4FZ7Aod4u/HUpaO4aXos/1p1hPVHCvlwWwYfbsvAVatmdKQP46L9CPd1p6DSRH6FibwKE0VVtcQHezrG4uEqF3OEEH1QGwXdOsLTxRNfV1/Ka8vJqcoh0b/lQq9tFr9z1JZZ2Klzbz1RAsDQMG806jNYk8YrtCGYb34B5LLxkTyz5ghH8g0kZ5QxIda/S6fSqDX4uPpQXltOqalUgnkhhDhNl4J5rVbL//73Py644ILuHJPoRo5e867uyh2G/GbBfEqJ8iFjkO8grh0xhHuz9rHs10xunz3Y+Q8IhSlQngFaNxg8x+nxD1THCgz884cU0hpSJO10GhVTBwewaFQYk2L90Z4y822z2cgoMbLpWBEfbE3nRJGyr1oFwV5u5FeaWHkgn5UH8tGqVdRbbU2OO21wIJPi/BkX7cfoSJ9OBdgJIV68d+NEtp4o5p1NaSRnlFFRY2Znehk701uuMny8sIpVB/Nx1ao5KzGIi8ZGsHBkqLS0FEL0HZ4N74fGEqivA61Lpw8R6RlJeW052VXZrQbzrRa/qy6GrF+V7zsZzK86qFyAOHd4SOcG3FVeYcCeFi+A+LjruGhMBCt2ZfHx9owuB/OgpNqX15ZLRXshhGiB08F8eno6s2bNkkC+j7On2RdqG17qFirQnjpjcP7oMJ74/jA55TVsSC1k7jAnPySkNszKDzoLXLq+bq6vMJkt7EovI9pfT3RA59Ma6y1W3tx4kpd/OkadxdriNieKqvlkeyYBHi7MHxlKmLcbe7PK2ZNVTml1nWM7L1ctV02M4oZpsUT6uXMwp5KVB/NYeSCPjBIjLho1M+MDWTQqjHnDQ/Bx7/paw2mDA5k2OBCr1UZaSTW7M8rYnVlOaXUtod5uhPq4E+7rhq/ehV9PlrDyQB7pJUbWHCpgzaECLkmK4OnLRreaCSCEEGeU3h/UOrCalfdH36hOHyLSK5KDJQdbLYJntpo5VnYMaGFm/ugawAaho8EnssPnrDCa2dYwM79wZGg7W3ezdpYmXD8lhhW7slh1IJ+Hz6/t8pI9P1c/0kiTYF4IIVrgdDAfHBxMUFBQd45F9AB7Abwie+zUwpvvqcG8m07DlRMieXtTGp9sz3A+mD/SsAawkzMNfVWRoZZPtmfw318zHO3bRkZ4s2hUGItGhhHbgUI/R/Irue/z/RzIqQBgztBgbp89GK2mcaa6rLqOHw8VsOZwPiXVdSz7NbPJMVw0akZGeHP+6HCunBiF5ymz66MifRgV6cP98xPJKDHi7+mCt1vPFAtSq1UMDvJkcJAnV0xo+cPv7IQg7pufyOG8Sr7dl8s7m9L4ak8OhQYTr18/vsfGJoQQHaZSKTPNFZnK+6MTwbyj13wrwXxaRRp11jo8dZ5EeEU0ffDoKe1bO2FtSgH1VhuJIV5nvihpO0sTRkX6MCbKl31Z5Xy2K4s7zurCkj0gwF0pyCsV7YUQojmng/mFCxeyZs0arFYrarXMsvVV9pn5CqzUqsC1MKXZNvY0e3u7nGsnx/D2pjQ2HC0iq9TY+T7jFdmQuxtQQUL/DeZNZgv7syv4fFcW3+zNdcykB3i4UGas42BOJQdzKnlmdSqjI32455wEzkoIapZGbjCZeeOXE7y18SRmiw1vNy2PXjCCS8dFtJhyPndYCP+0jGTbiRJWH8qnylTPmChfxkX7MjzcG1dty0Xu7FQqVYcuLpwJKpWKEeE+jAj3YdrgQO74JJktx0u48o1tvH/TRMJ83Ht7iEKI3zqv0IZg3rl18/aK9tlVLQfz9hT7RP9E1KpTPi+ZTXB8vfJ9Jy98rz6oXJhfcKZn5aFDRQOvnxzNvqxylv2aye9ndWHJHo3t6cpqZWZeCCFO53Qw/+ijj/Ldd9/x5z//mRdeeAEXl86vMxM9z9vFGxe1C3XWOoo0GiL3LYO5DztS30tNpRQYldT7RD9lrV9coAcz4wPZdKyY//6ayQMLW6i+25ad7ypfY6Y39vDtIworTezOLFOqumeVY7ZYCfNxI9TbnTAfN7zdtaTkGdidWcbh3Mom686Ton25ZUYcC0aENrZvO5jH1hMl7M+u4Kb3dzJ9SAAPLhzGyAgfzBYrn+7I5OWfjlHSkB4/b1gIT14ykmBvtzbHqdOomZUQxKyEgZP9MjshiBW/n8pNH+zkSL6BS1/byns3TmRYWMerNwshRLfraq/5dtrTOS6Yn55in74JzNXgFQ5hYzp8vqraejYeKwJg4ajeCObbLxp4wZhw/vlDCtllNfxytJA5Q53/LGBvT1daIzPzQghxOqeD+fDwcDZv3syFF15IYmIiZ599NtHR0S3O0qtUKh5++OEuDVQ4R6VSEaQPIqcqhyK/aCKL0mDfpzDxVqCxXU6Md0yTljrXT4lh07FiPtuVxT3nxLc7G+xQZ4Tk95Xvp/yhW5+Ls2w2G//9NZPXN5wgp7ym2eN72tg3yMuVaYMDWDI1lvExfo77AzxduXZyNNdOjqakqpY3N57kgy3pbDlewgX/2cx5o8I4nFvp6AE8KNCDvy4cyrnDQ37TBeBGRvjw5R+mceP7OzhRVM0Fr2zmusnR/HluPAFdbYUohBDO6GKveXuafY4hB6vN2nT2nTaK36WeshytE+8LPx8ppK7eSmyAnsSQXugi1IGZeTedhivGR/LO5jQ+2Z7ZLcH8QJmZN5jMbDxaTG55DXkVJvIrla8qIMzHnVAfN8J83Ij0c+esxGDcdB38/CWE+E1yOpi32Wy8/PLLHDlyBKvVygcffNBsG5VKhc1mk2C+lwXrg5VgPmEeFL0Nv74JE24BlcqxXv70DxlzhwYT5uNGXoWJVQfyuTgpoqVDN7d/OdSUgV9sl9fL788u58OtGeSW1zA60oekaF/GRfu1O6t9KmNdPQ9+eYBv9uYCSuX3hBAvxsX4kRTli5ebjvwK5Y00r8JEmbGOIcGeJEX7MS7alwhf93aD7wBPV/62aBiLp8Tw7JpUvt2Xy/f7lRmLAA8X7p4Xz9WToqUne4Mofz3/+8M07v18Hz+lKK3uvtydw+1nDeaWGXHywUUIcWZ1sdd8qEcoGpVGyYAzFhHi0Ri42mw2UktTgdPeZ222U1rSdW69fGOKfVjvXBy2X/yoLgKLGTQt1z+5bkoM72xO4+fUQueW7DXwd1Mq4ve1NfOl1XWk5FUyMtwHH337NWBaytZrrrzJT7fPHtz57EghxG+K08H8s88+yyuvvIJWq+X8888nPj4eT88zXIRFdIijPV3IMHDxguKjcGI9DJnbau9brUbNNZOieWHtUT7ZntGxYN5qhe2vA7BcvYg9Xx7ihmmxDA/veBq1xWpj7eF83t2c1qTl2baTJY7vI3zdGRTkQai3cvU61MedKH93Rkf4NnlDPV5YxR8+SeZYYRUatYq/Lkjk2skxTYrGdacofz3/viaJW2bE8dbGkwwO8uB3swbhJYXemvHVu/DODRPZeryY/1uZwqHcSp5dk8rH2zI4e2iQ42LKoEBP1Geyf7IQ4reni73mdWodoR6h5FTlkFOV0ySYP1F+AoPZgJvGjUG+gxp3yturnE/nAbEzOnwuk9nCz6mFQC9UsbdzP60DQCtV+E9dsrdsRyZ/XeBcUOqYme/lavb5FSbWphSwJ6OMPVnljtaykX7ufHDTJIYEt/wZ2Gaz8ePhAp5edcSRrRftr2dslG/DZxjlswygzNRXmDicV8mmY8WsP1IgwbwQok1ORzXvvPMOer2eTZs2kZSU1J1jEt0sWK9UtC80V0LSdfDrG8ptyFxH+l+ztXzA1ROj+Pe6Y+zKKOPnI4WcPTS47ROdWAfFR6nCnX/mjKMqJ4sVu7KYNjiAW2bEcXZicKuBmcFk5rNd2XywNY2sUiUVXqtWccGYcCbF+bM/u4I9mWUcLTCQU17TYro8oMyqR/kS5a/nzV9OUF1nIdjLlf9cO45JcV3vd9sRY6J8efW6cWfkXP3dtCGBfPfHGXy9N4fn1qSSW2Hi0x1ZfLojCwBvNy3njgjlkQuGS/V7IUTP8OzamnlQ1s3nVOWQXZXNuJDG//+35W0DYFzIOHTqU/4Ps8/KD5kDuo5nm/1ytAhjnYUIX3dGR/o4Pd4uUasbigZmKb+zNlrq2ZfsrdiZxZ/mDEHv0vmPnY4CeL0YzK85lM+9n+3DUFvf5H4PFw3ZZTVc9vpW3rlhAhNjm37O2JNZxpMrUxyTEx3N1iurriPpibUcLaiiyFBLkJcsQxNCtMzpYD4rK4uzzjpLAvl+wF7RvshYBJNuU9Lsj/1IVf4BMiozABga0PzKb7C3G9dPieGDreks/WwvP/x5JuG+LVcft1htZP3wHLHA8vqziIsIIzpAz+qD+Ww9UcLWEyXEBuiZGR/kSJePCdCTXVbDB1vTWbEzi6qGN0lfvY7rJkezZGosIQ0p9ddMUs5TVVvPwZwKsstqHOnx+RUmjhdVkVFi5HhhFccLqxzjmhznzyvXJhHs1fEPS+LMUqtVXDoukkWjwvjlaJFSoDCjnP055VSa6vkiOZtd6aW8fv14KZYnhOh+jpn5LgTznpH8yq/N2tNtz9sOwNSwqU136GKK/fwRob1bf8URzLedzTB3aDCxAXrSS4x8tC2D22cP7vSp7Gn25bXlWKwWNOoztxSr3mLl2TWpvLnxJADDw7yZNzyEcdG+jI3yxWK1cetHu9iTWc517/zKi1eO5bzRYWSWGHl6zRF+aFhy56pVc+vMOG6fPbhD2Xp+Hi4MD/PmcF4l206WcOGY8B59nkKI/svpYD40NBQvr14ovCI6zZ5mX1hTCAGDIf5cOLaG1O0vAxCiD3G8WZ7uwUVDSc4o40BOBX9ctpsVv5/a7GpyaXUdz378NU+Vb8diU1E28iY+v2wqbjoNOeU1fLQ1nWU7MkkvMZJeksHH25ULCL56HZU1ZuwF44cEe3Lz9DguSYrA3aXlN2tPVy1TBgW0+FhJVS17s8odlejHx/g19HGXter9gZtOw/wRocwfoaSOmi1WdqaVct8X+0kvMXLJa1v458WjuHx867NAQoiBIyWvkoM5FY6aJvkVSkbWjdPjmN2dnT7sa+ZrSqG+FrSdnwVtqaK92WJmZ/5OAKaET2ncuCIb8veDSq28H3dQXb2Vn1KU7jO9UsX+VB2sM6DVqPnTnHj+8vk+3tp4ksVTYvDo5FI3XzdfAGzYqKiraPXzSmuO5FeSkldJbrly8T+vwkRVrZkpgwI4b1QY8a0UESw0mPjjsj3sSFPW6t86I46/Lhza7DPQsluncNfyPfx4uIA/frqbb/eFsP5IIWaLDZUKLhsXyV/OTeh0K9bpQwKUYP5EsQTzQohWOR3MX3LJJXz66aeYTCbc3GTWsy9rMjMPMOV2OLaGlIz14Ovh6C/fElethlevHcd5r2xid2Y5z65J5W+LGrdfcyifh746yF9My0AL+WFzue/q+Y7HI3zdeXDRMP48N54NqQ2zrpllHMyppNxoBmBmfCC3zIhjVnxQl9ZHB3i6MndYCHOH9a12eMI5Oo2aaUMC+f5PM7h7xV5+OVrEvZ/vIzmjjEcvGC6F8oQYwP6XnM1fPt/X4mM/pxYxMz6QBxcOa7MmS3VtPeuPFLL6UD7lxjpCvd0J91XWKIf7uDM+1k9ZvuPuBxpXsNQqgXZA09ljq9VGSXUd+RUmXHVqEloI/iI9G3rNnzIzv794PzX1Nfi7+ZPgl9C48YEvlK9Rk8EjsKO/EraeKMZgqifIy5Xx0X7t79CTOlFn4KKx4fzn5+OkFVfz4bZ07jhrSKdOpVPr8HbxprKukjJTWYeD+bTiap5ZfYRVB1u+4LD9ZCkv/XSMIcGeLBoVRnywJwWVpoaLRzXsSCuluKoOT1ctz1w+mkWjwlo8jruLhtevH8/j3x3io20ZrDmkXHDpyN9oW6YNDuTtTWlsOV7S/sZCiN8sp4P5xx57jDVr1nDNNdfw9ttvExjY8TckcWYFuytr3R3B/KCzITCRFJSfW1ovf6roAD3PXj6G2z9J5q2NJ5kY68/4GD8e+/YQ3+7LxZ9KLnPbDEDEwntbPIaHq5bzRodx3mjlzbC23sKRPAM+7jpiAz2642mKAcrPw4X3b5zIK+uP89K6o3y6I5ODORW8dt04p6sjCyH6rtR8Aw99fQCAcdG+DAn2JNTHnXAfN44WVPHx9nQ2HStm8/FNXDYukgvHhKM+JeW8uKqW1Qfz2XC0EJPZ2up5XDRqZiUEsnBkGBeFjEKbuwvT2n/yy6in2JNZzr6scrLLjRRU1FJnaTzONZOiePSCEU0uKNpn5k8N5rflKuvlJ4dObmxXV54JG59Vvh9zdYd/Jzabja/2KLP+5w4P6f3CoJ3oAKDMzg9h6WfK7PySqbHtFqI9mFPBC2uPUlGjXPSvcXMHdSV/+2Ybt0/2YnZC6xf/S6vr+Pe6Y3yyPYN6qw21CibG+hPh506YjxthPu6oVSp+Silg07EijhdW8e91x1o8VkKIJ69fP57BQW0XeNaoVTx+4QgGB3my/kghN8/oevbIxDh/NGoVmaXGLnUDEEIMbE4H83fffTeJiYl8/fXXrF+/nvHjx7fZZ/7dd9/t0kCF8+wz8wazgZr6Gty17jD596TsfQaAYZb2PxQsGBnKzdPjeG9LGn/5bC8uWjXFVXWoVfCfQTtwyTFDeBJET2n3WKDM+I+J8nX6OYnfFrVaxV3z4hkb7ctdy/dwIKeC81/ZzEtXjW2/MKMQot+orq3njv8mYzJbmRkfyIc3TWoWtN04LZZn1hzh+/15fJGczRfJ2a0cDWIC9CwaFcbgIPusa41SZ6WwivQSIz+lFPJTSiHLtBfxmXY3bke+5H/7Y/jROrHJcVQqCPR0pbiqlk93ZHEgp4LXrxvvCLDsveYLawox1Ztw07o51ss7UuxtNvj+HqirgqgpkLSkQ78Ti9XGo98edLRYvWhsB1vF9qROdgC4cEw4/1l/nJPF1Xy4NZ07z259dv6Xo0Xc8Uky1XUWx33uMW5o9ZCcncVNKTsZFOTBzdPjuGxcJK5aNccKq9iTWcbuzDJWHch3FKo7KzGIBxcOIzG0eTbFtZOjqagxsy6lgNUH8yk3mh2V5cN83Ijw0zMzPrDDWWAqlYobpsVyw7TYDm3fHk9XLWMifdidWc62kyUSzAshWqSy2Ww2Z3ZUq9WOPvLtnkSlwmKxtLtdb6qsrMTHx4eKigq8vQdWkS2bzcbkZZOpqa9h5SUrCfcM5609/+H1A29jU6lYm5lDaNwcmPsIhI1u9Th19VaueHMb+7LKAUgM8eK9EXuI2PqIssFl78Koy8/AMxK/ZTnlNdzx392Ov8M/zxnCXfMS0PT2TJUQoktsNhv3rNjL13tzCfF2ZeWfZxLg2fr69T2ZZfxn/fFm3U1ctWqmDwlk0agwRoR7t1gozmazcbSgih8O5LHyQB7HC6v4q/ZT/qD9jlKVL/8Z+gmJg2IYHORJqI8bId5u6DRqNh0r4q7leymtrsPbTctLV49lztAQbDYbUz+dSrW5mm8u/oYg9yBmLp+JxWbhx8t+JMwzDPYth69+r6T0/2ELBMa3+zupqbPwp0/38FNKASoVPHzecG6eEdf5X253O7EePr4EgofDHds6tMvXe3K4e8VefPU6Nt1/douF4D7blcWDXx7AYrUxbXAAS6bGolLBB8cf42D5FsZ53MzuA8Mdwbq3mxabjWZV5oeHefPQecOYPqR/Z40+/2Mqr6w/zsVjw3npaik4LcRvSUdjU6dn5t9//31ndxVnmEqlIsg9iExDJodKDvHotkeVojwqFde5RhJqzYHja5XbiEvhnMfBN7rZcVy0al67bhyPfH2QURHe/FH3DdoN/6c8OOk2GHnZGX5m4rcowtedz34/hX9+n8LH2zP49/rjbD9ZynVTopkzNLjFD4j5FSaKq2qJD/HEVev8WnubzcaJoipiAzyksKIQ3Wz5ziy+3puLRq3ilWvGtRnIAyRF+/HujRPb3KY1KpWKxFAvEkO9WHpOAscKDJRUJGFZk4p/yVEe0X4EE99utt/M+CC+/9MM7vjvbvZmlXPzB7v449lDuOecBCI9I0ktSyXbkE16RToWm4UY7xglkK8qhNUPKAc566+OQN5ms1FuNJNbUUO9xUaojxuBnq5o1CpKqmq55cNd7M0qx0Wr5uWrxrKwlXXbZ1wnZ+YBLhgTzivrj3GiSJmd/+OcxosZNpuNf687zos/HQXg4rHhPHP5GFy0yv+z2ypCOVgOU4a48Z/z5/D5rmzeP6WVrbtOw5goH8ZF+zExzp/ZXazB01dMHRzAK+uPs/VECTabrXc7GAgh+iSng/kbbrihO8cheliQXgnm/7rpr1htVty17jw85WEuGHwBlJyAn/8PDv4PDn0Jx9bCwqdh7LVKbuEpInzdefeGCfDj32HDf5Q7Z/8Vznqw2bZC9BRXrYYnLh7J+Bg/HvzyADvSS9mRXoqLVs2s+CDmjwihosbMnsxy9mSWkVthApQ1siMivBkX7edokRjm49ahD0gms4W7lu9hzaECRkf68OzlY1pM3RSiv7FabXy8PYMd6aUMC/UiKdqPMVG+7a5rbktdvZWUvEp2Z5axN6scH3cdv589mIhW2pseyq3g0W8PAXDvuYlMiutcxfKuig/xUqqa61+Hd8+BA5/BiIth6HnNtg33deez30/lyZUpfLA1nf/8fJw9WWUEx4Q3BvOV6QBMCWtIsV91P9SUYQ0Zxf9cLuGbd34lu8xIXoWJ2vqm6/q1ahUh3m7U1lsorqrDV6/jnSUTmBB7Zn8nbXJ0ACgDswl07RdC1qhV/HluPHct38vbm9II93WnoLKW/IoaUgsMbD+pVI2/46zB3Dc/scn/y/aid6WmUrzcdNw8I44bpsWSnFGGh6uGxBCvAXmBdVy0H65aNYWGWk4UVTMkuO21+0KI3x7n36lFv2JvT2e1WRnqP5RnZz1LrE+s8mDAYLj8PZh+N/zwF8jeAd/cAakr4fyXwLOhiIvVCsWpsOXfsG+Zct/8p2DqHWf66QgBwMVJESRF+/JFcjY/HMjjZFE1P6UUONo32alV4OWmOyXAL3c8FuLt6gjux8f4kxTl22xGp7S6jls/3Mnuhv32Z1dw/iub+NOceP5w1uBmrYqEaIvVauNooQFXrYZQb7dWW3G250B2BR9vTye33NTkfhetmsvGRbJoVPu9yCuMZpZ+tpd1RwoBHH2xVSplKdXl4yO5cVpsu4FSfoXJsWZ5T2Y5B3IqmgWpy3dmcfP0OO44e7BSRR44VmDgvS1pfLk7h7p6K3OGBvP7WYM69XvoVpETYNqfYMvLyvr26Kmgbx5Eu2jVPHbhCJKifXngfwfYcrwE/1obeCrt6ezF76aGTYWU7+HQV1jRsLh4MVu+Sml2vAAPF7QaFUWGWuqtNsfSgUg/dz64aVLfC+LcfEHrBvUmqMoHv9gO7Xb+6HBeWX+c44VVLP2sabcCtQr+cdFIrp8S02w/Pzelen9ZbZnjPo1adcYv+pxpbjoNE2L92HK8hG0nivve34EQotc5vWbeLi8vj2+++YbU1FQqKytbXEPfHwrgDeQ18wD/O/o/ntj+BFcmXslfJvwFV00r6YtWi/Ih5ucnwWoGfSBMvBUKDkLGVqUPLyj9cS98BZKuP3NPQog22Gw2UgsMrDyQz8ajRQR6upAU7ce4aD9GR/qgd9GQWWpkT2Y5uxuCjpQ8AxZr0/+zhgR7ctP0WC5NisTdRUNmiZEb39/ByeJqvN20/Ouy0Xy5O5ufUpTgZ0S4N89ePsbp9kMDUXpxNeG+7o4U2b7EarVRXFVLcVUdcYEeTgfSNpuNypp68iprCPN2x0fffHnHqSxWG8kZZaw8kMeqg3kUVNY6HvPV6wj1diPaX8+YKF/H32xL/bgtVhtrDxfw3uY0dqSXtnnOpGhfHlo0rNUZ3YM5Ffzhv8lkldbgolVz07RYcspr2JNZ3mQd+pgoX567fHSzftz7ssr5aFsG204UO7JfTuWr15EU5cvYKD+2nSx2zLz66XXcND2O5Iwyfjla5Nh+fIwf7yyZgJ+HS5vPq8eZTfDmTCg+CjEzlIvdXq23PD1aYOD2T5LJqv8Jt9BvCHIZTFHdCVSoWez2GLcdvQcfSxmv1l/Is/VXE+bjxuKpMYyP9iPMx51gb1dHkbV6i5WiqlryKkxUGM1MiPVrcelQn/DyGChLh5vXdLj4LcC2EyU8uTIFLzftKQXn3Bkf48ewsJb/H/3h5A88sOkBJoVO4t35ffvzZHd79efjPLsmlQUjQnlj8fjeHo4Q4gzpaGzapWD+lVde4b777sNsNjvusx/OPhtgX+MjBfB6X52lDhdNBz8k5e1XCvUUHm56v9YdoiYpMxfx53T/IIU4g2rqLOzPLmdPVjm7M8rYeqKEqoZCSn56HZeNi+TrvTkUV9UR4evOBzdNJD7EC5vNxjd7c3nsu0OUG82oVXDVxGjuOSeeYK/m6aaVJjNpRdW095+tVq0i2MuVQE/Xfrne02az8c8fUnh3cxrBXq4smRrDtZNj8O9icFZYaWJ3w5KJnPIazhkewqJRYR3KiDCYzLy9KY2tx4vJqzBRUGmivuECjo+7jmsnR3PD1FhCfdpOE7ZYbXy9J4ev9uSQW15DXoWJGrPyvqZ30fDgwqFcNzmm2etWUWPmzV9O8HlyNkWGxgBe76LBZsNxjNOpVTA01Jsgr6YXXk8WVznWCWvVKs4fHcbsxKAmrdmOFVTx7uY0x7EXjAjlxumxTapyH8gu54kfUqirtxLp584b149nZISP4/HCShM/Hi7g6dVHMJjqcdGouWtePLfOjGN9SiHvbk5jV0bjLKl9vPblK0nRvsQFejT5LLD+SCFPrkzhRFG1Yz+VSmm1dsuMQUyM9es7a4Kzk+HD88FsBI8guOQNGDKv1c2rauv53ecfc9DyguM+bU0Iq/OOEaIq54g1ioeDX2HJzEQWjAwdGNk87y2AzG1wxQcw4pIePdXW3K38fu3vGeI7hK8u+qpHz9XX7M4s49LXtuLjrmPPw+f0y/cGIUTn9Xgwv27dOs455xy8vb258847+eWXX9i2bRtvvPEGR48e5csvvyQ9PZ277rqLMWPG9Pk19r+FYL7T6muVWfr8/RAxHmKmQ9hY0PbyrIkQPcRgMrNiZxYfbE0nu6xxZnJ4mDfv3zSREO+mAV+hwcRj3x5i5QGl17LeRcPvZw3md7PiMFuUGdRVB/LYdKy4SZ/q9tjXzIb5uDE4yFMJkGL8GBLk2eYHOavVRkl1HeXGOmIDPZwOGExmC5uOFfNzaiHR/npumh7bbuFAm83G498d5oOt6U3ud9WquXRcJDdNjyUhpPUaA8a6eg7lVpJbrrQOy6swkVtew6HcymbVygHCfNxYMjWWaydFtzgrbrZY+XRHJi//dIyS6romj6lV4OGidVTA1qpVnDc6jBumxTIm0rdZZ4RNx4p4cuURUvIqm53Hw0XjaKE1ZZA/T182mpgAD+rqrXyyPYNX1h+jzKhc8PZy03LO8BDOGxXGjPhAXDRqKk315FeYyK2o4URhVbM6Dy3x1eu4dlI0S9q4CFFYaeLFn46yYmcW1jbe5ecODeaFK8e2mlmQX2Hib18dYH1DGr6bTu3o3a7TqLhgdDiXj49kTJRvi5kEp6u3WFmxK4tv9+YyLMybm6fHER3QR1tuFR6BL26GQmUtP9P+BHMeafU98GT5SS765iLHzzdU1HJvaQFlnkPIuXA5IxPar17fr3x+Ixz6Chb8C6b8oUdPdaT0CFd8dwX+bv78ctUvPXquvqbeYmXsP9ZSVVvP93+a0eSimxBi4OrxYP7CCy/khx9+YOvWrUyePJmbbrqJjz76yDEDX1tbyx/+8Ae+/PJLdu/ezaBBnV8D9+qrr/Lss8+Sn5/PmDFjeOWVV5g0aVKr23/++ec8/PDDpKenEx8fz9NPP82iRYs6dC4J5oUQdvUWK2sPF/DJrxn4e7jy5CUj20x13ZFWyv+tTHG0y/PT66iqrcdsafzvNdjLtd2087p6K8VVta0GX16uWkZGKEsGTmUwKeneBRW1josGsQF6/rpgKAtGtr9uut5ipdBQy4GcClYeyGNdSqEjQwEgPtiTZ68Yw9go3xb3t9lsPPbtIT7clgHAExePxNNVw7ub0ziY0xgADw31YuHIMM4bHcqQYC+qa+v5ObWQlQfy+PlIUZuz1AkhSmE2X72Oz3dlUVylBOjuOg1zhgYT4edOaMMFkDqLlZd/OsbJYmUGeFCgB7efNZghwZ6E+bgR5OmKSqXip5QC3t2cxo60xnR1DxcNY6J8SYr2ZWioN18kZztSwb3ctNw+e7CjcGKojxsuGjUfb8/gX6uOUGO24K7TsGRaDKsP5pNRYgSUpRt/OSeBOcOCO9xNIb/CxN6scqpPa7nl4aphdkJwh5cHHC0w8PyPqRzKbXohQqdRc9XEKG6bOajdmT6bzcaXu3N4/LtDVJrq8dPruG5yDIunxjS7wDXgmGuUgq8731F+DhsLcx6GIXObFX2ttdQy4ZMJjp/fyytgom8CLP4GPALO4KDPkNUPwvbXlHo75zzeo6cqqC5g3hfz0Kg07F68G7VqAGQ2dMItH+xk3ZFC/rZoKLfNGtzbwxFCnAE9HsyHhoYSFRXFzp07AZoF86AE9DExMSxYsIAPPvigU8dfsWIFS5Ys4Y033mDy5Mm89NJLfP7556SmphIcHNxs+61btzJr1iyeeuopzj//fJYtW8bTTz/N7t27GTlyZLvnk2BeCNEVNpuN7/fn8cyaI4406IQQTxaNCmPRqLA2Z6VPZQ+s7TPTh/Mq2ZNZxr6silaD3VOpVErVfnvxsfExfvxt0TDGx/hRW2/hYE7D8bIryCw1kl9RQ5Gh+QWEUG835gwL5sdD+RRX1aFWwe9mDeKeeQlN0rVtNhuPfHOIj7dnoFLBvy4dxVUTox2P7Uwv493NJ1mXUuhIbwflYkN+pckxy2s/Z0yAXllD66sE50OCPZtVVq+tt/Dt3lze3ZzGkXxDq7+LAA8X7p4Xz9WTotvMUjiYU8G7m9P48VC+Y5b9VDqNiuunxPDnOfGtrufOLDFy///2OdaFAwR6urL0nASunBA5ICptFxlq2ZtVzowhgU7XGui3Ur6Db+4EU4Xyc9AwpfjrqCuVSu6mSsjbx9wt91JoMeJutbLZHITL4q/A3a93x95TtrwMax+B0VfDpW/26KnqLHWM/0RZL77pqk34uvn26Pn6mnc2neSfP6RwVmIQH9zU+qSWEGLg6PFg3tXVlUsuuYTly5cDcNttt/Huu+9SVVWFu3tj65krrriC7du3k5WV1anjT548mYkTJ/Kf/yjtz6xWK1FRUfzpT3/igQceaLb9VVddRXV1Nd9//73jvilTpjB27FjeeOONZtvX1tZSW9u4frGyspKoqCgJ5oUQXVJbb2HbiRIi/dwZEtx9revqLVZSCwwcaaFon7uLhnBfN0J93An2cqW23spbv5zgrU0nHcFyQogn6cXGVtP9dRoVkX565gwNZtGoMEdV/7LqOh777hDf7M0FICZA3+TCRFl1HbsyylCp4OlLR3PlxKgWj19hNPPj4XxWHcxn07EiR9ZCTICeRaPCOG9UGCPCvTu1Ztpms7EjrZQDORVKan6libzyGipqzCwYGcrtswd3qniYxWrjWKGB3RlKqvvB3EoSQjy5Z14CsYEe7e5vtdr4745M/rs9g3NHhPL7WYM6lHou+onKXNj6Cuz+COqqlPs8gpTK7iXHALghLJjdbm5Mt7rwxjXrwW0Ap0Tv/wy+/J3yO4js+QBzat0hqrAyReWJO/3/4lhn1FutlFbXOS7WCiG65taJDzNz3IW9PYw2dTSYd/pThr+/P9XVjUVs/PyUK8+ZmZkkJiY67rdYLJSUlHTq2HV1dSQnJ/Pggw867lOr1cybN49t27a1uM+2bdtYunRpk/vmz5/P119/3eL2Tz31FI8/3rNpYUKI3x5XrYazEptnD3WVVqNmRLgPI8LbDw50GjVLz03k2skxvLA2lc+TszlaoAQfAR4uJEX7khTtx+Agz4aLAG4EerRcdM/Pw4WXr07ivFFhPPT1QTJKjI70cTuVCp65bDRXTGg5kAfw0eu4YkIUV0yIoqLGzK8nSwj3de90AN/0vComDwpg8qDuSWHWqFUMDfVmaKg3106O7vT+arWKxVNiWNxCay0xAHiHw4Kn4KwHIPlD+PVNqMyG6oaK/D7RDPcIYreliDmT7hrYgTxAYEMNgOoiSP2hx08XEx7CIVdXttuqevxcfY4KkK50QnSb4orc3h5Ct3E6mI+Ojm4y2z5y5EglzfT77x3BfFVVFZs2bSIyMrJTxy4uLsZisRAS0rQVTEhICEeOHGlxn/z8/Ba3z8/Pb3H7Bx98sEnwb5+ZF0KIgSLUx41nLh/DbbMGc7TAwIhwb6L99U4Fz+eOCGVyXAA/pRQ06x8+KsKHUZEdD1x83HWcOyK002MQok9w84Hpf1aKvp34WbmaFZ4EHoHcWVfFzKL9TA2f2tuj7HnhSXDd/6Cic5mXznq+roJtVRntdgUZqAy19RRX1ba/oRCiXSMGdbydZl/ndDA/e/ZsXnzxRQoKCggJCeG8887Dw8ODv/3tb+Tn5xMdHc2HH35IaWkpV199dXeOuVu4urri6tpKr3UhhBhAhgR7MiS469M6Pnodl43v3MVZIQYsjQ4Szm1yl6eLJ9MipvXSgHpBfOvt+rpbBHD5GTubEEL0D04H81dccQV79uxh7969zJ8/H39/f1544QVuv/12XnhB6bNqs9mIjY3tdDp7YGAgGo2GgoKCJvcXFBQQGtrybE5oaGinthdCCCGEEEIIIforp6toTJw4kbVr1zJ//nzHfb/73e/YuXMn999/P7feeisvvPACe/bswd/fv1PHdnFxYfz48axbt85xn9VqZd26dUyd2nLq2tSpU5tsD7B27dpWtxdCCCGEEEIIIfqrbi+zO27cOMaNG9fl4yxdupQbbriBCRMmMGnSJF566SWqq6u56aabAFiyZAkRERE89dRTANx1113Mnj2b559/nvPOO4/ly5eza9cu3nrrrQ6dz17Uv7Kysp0thRBCCCGEEEKInmGPSdtrPNdne+ZcddVVFBUV8cgjj5Cfn8/YsWNZvXq1o8hdZmYmanVjYsG0adNYtmwZf//73/nb3/5GfHw8X3/9dYd6zAMYDEqvYimCJ4QQQgghhBCitxkMBnx8Wi8y7HSf+YHGarWSm5uLl5eX022SzhR75f2srKw2+w6Kvk1ex4FBXseBQV7HgUFex4FDXsuBQV7HgUFexzPPZrNhMBgIDw9vMoF9uj47M3+mqdXqTrfQ623e3t7yD2oAkNdxYJDXcWCQ13FgkNdx4JDXcmCQ13FgkNfxzGprRt7O6QJ4QgghhBBCCCGE6B0SzAshhBBCCCGEEP2MBPP9kKurK48++iiurq69PRTRBfI6DgzyOg4M8joODPI6DhzyWg4M8joODPI69l1SAE8IIYQQQgghhOhnZGZeCCGEEEIIIYToZySYF0IIIYQQQggh+hkJ5oUQQgghhBBCiH5GgnkhhBBCCCGEEKKfkWC+j3r11VeJjY3Fzc2NyZMns2PHjja3//zzzxk6dChubm6MGjWKlStXnqGRirZ05nV8++23mTlzJn5+fvj5+TFv3rx2X3dxZnT236Pd8uXLUalUXHzxxT07QNEhnX0dy8vLufPOOwkLC8PV1ZWEhAT5v7UP6Ozr+NJLL5GYmIi7uztRUVHcc889mEymMzRa0ZKNGzdywQUXEB4ejkql4uuvv253nw0bNjBu3DhcXV0ZMmQIH3zwQY+PU7Sts6/jl19+yTnnnENQUBDe3t5MnTqVNWvWnJnBijY582/SbsuWLWi1WsaOHdtj4xOtk2C+D1qxYgVLly7l0UcfZffu3YwZM4b58+dTWFjY4vZbt27lmmuu4ZZbbmHPnj1cfPHFXHzxxRw8ePAMj1ycqrOv44YNG7jmmmv4+eef2bZtG1FRUZx77rnk5OSc4ZGLU3X2dbRLT0/n3nvvZebMmWdopKItnX0d6+rqOOecc0hPT+eLL74gNTWVt99+m4iIiDM8cnGqzr6Oy5Yt44EHHuDRRx8lJSWFd999lxUrVvC3v/3tDI9cnKq6upoxY8bw6quvdmj7tLQ0zjvvPM4++2z27t3L3Xffza233iqBYC/r7Ou4ceNGzjnnHFauXElycjJnn302F1xwAXv27OnhkYr2dPa1tCsvL2fJkiXMnTu3h0Ym2mUTfc6kSZNsd955p+Nni8ViCw8Ptz311FMtbn/llVfazjvvvCb3TZ482fb73/++R8cp2tbZ1/F09fX1Ni8vL9uHH37YU0MUHeDM61hfX2+bNm2a7Z133rHdcMMNtosuuugMjFS0pbOv4+uvv24bNGiQra6u7kwNUXRAZ1/HO++80zZnzpwm9y1dutQ2ffr0Hh2n6DjA9tVXX7W5zf33328bMWJEk/uuuuoq2/z583twZKIzOvI6tmT48OG2xx9/vPsHJJzWmdfyqquusv3973+3Pfroo7YxY8b06LhEy2Rmvo+pq6sjOTmZefPmOe5Tq9XMmzePbdu2tbjPtm3bmmwPMH/+/Fa3Fz3PmdfxdEajEbPZjL+/f08NU7TD2dfxH//4B8HBwdxyyy1nYpiiHc68jt9++y1Tp07lzjvvJCQkhJEjR/Lkk09isVjO1LDFaZx5HadNm0ZycrIjFf/kyZOsXLmSRYsWnZExi+4hn3MGJqvVisFgkM85/dT777/PyZMnefTRR3t7KL9p2t4egGiquLgYi8VCSEhIk/tDQkI4cuRIi/vk5+e3uH1+fn6PjVO0zZnX8XR//etfCQ8Pb/YBRpw5zryOmzdv5t1332Xv3r1nYISiI5x5HU+ePMn69eu57rrrWLlyJcePH+eOO+7AbDbLB5de4szreO2111JcXMyMGTOw2WzU19dz++23S5p9P9Pa55zKykpqampwd3fvpZGJrnjuueeoqqriyiuv7O2hiE46duwYDzzwAJs2bUKrlXCyN8nMvBB90L/+9S+WL1/OV199hZubW28PR3SQwWBg8eLFvP322wQGBvb2cEQXWK1WgoODeeuttxg/fjxXXXUVDz30EG+88UZvD010woYNG3jyySd57bXX2L17N19++SU//PADTzzxRG8PTYjftGXLlvH444/z2WefERwc3NvDEZ1gsVi49tprefzxx0lISOjt4fzmyaWUPiYwMBCNRkNBQUGT+wsKCggNDW1xn9DQ0E5tL3qeM6+j3XPPPce//vUvfvrpJ0aPHt2TwxTt6OzreOLECdLT07ngggsc91mtVgC0Wi2pqakMHjy4ZwctmnHm32NYWBg6nQ6NRuO4b9iwYeTn51NXV4eLi0uPjlk058zr+PDDD7N48WJuvfVWAEaNGkV1dTW33XYbDz30EGq1zGn0B619zvH29pZZ+X7o/9m77/CoyuyB49+ZzGQmbVJJL0DovfcqTREUxa6IunZddVEsqyu2VVexi8tvbaAo2BALiCDSeyAU6S2EJKSQOunJzP39cTOTDEkgCUkm5XyeZ54kt76Tm3LPPe973iVLlnD33Xfz3XffSe/DZshsNhMTE0NsbCwPP/wwoN7rKIqCTqdj1apVXHbZZU5uZesh/8WaGFdXV/r378+aNWvsy6xWK2vWrGHo0KFV7jN06FCH7QFWr15d7fai4dXlOgK88cYbvPzyy6xcuZIBAwY0RlPFBdT2Onbp0oX9+/ezZ88e++uqq66yV2COiIhozOaLMnX5fRw+fDjHjx+3P4wBOHr0KCEhIRLIO0ldrmN+fn6lgN32gEZRlIZrrKhXcp/TcixevJg777yTxYsXc+WVVzq7OaIOTCZTpXud+++/n86dO7Nnzx4GDx7s7Ca2Lk4uwCeqsGTJEsVgMCgLFixQDh48qNx7772Kj4+PkpycrCiKosyYMUN5+umn7dtv3rxZ0el0yty5c5VDhw4pc+bMUfR6vbJ//35nvQWh1P46vv7664qrq6vy/fffK2fPnrW/zGazs96CUGp/Hc8n1eybhtpex/j4eMXLy0t5+OGHlSNHjii//vqrEhgYqLzyyivOegtCqf11nDNnjuLl5aUsXrxYOXnypLJq1SolOjpaueGGG5z1FoSiKGazWYmNjVViY2MVQHn77beV2NhY5fTp04qiKMrTTz+tzJgxw779yZMnFXd3d2X27NnKoUOHlHnz5ikuLi7KypUrnfUWhFL76/jVV18pOp1OmTdvnsN9TlZWlrPegihT22t5Pqlm7zwSzDdRH3zwgRIZGam4uroqgwYNUrZt22ZfN3r0aGXmzJkO23/77bdKp06dFFdXV6V79+7K8uXLG7nFoiq1uY5RUVEKUOk1Z86cxm+4cFDb38eKJJhvOmp7Hbds2aIMHjxYMRgMSvv27ZV///vfSmlpaSO3WpyvNtexpKREeeGFF5To6GjFaDQqERERyoMPPqhkZmY2fsOF3dq1a6v8f2e7djNnzlRGjx5daZ8+ffoorq6uSvv27ZXPP/+80dstHNX2Oo4ePfqC2wvnqcvvZEUSzDuPRlGkn5kQQgghhBBCCNGcyJh5IYQQQgghhBCimZFgXgghhBBCCCGEaGYkmBdCCCGEEEIIIZoZCeaFEEIIIYQQQohmRoJ5IYQQQgghhBCimZFgXgghhBBCCCGEaGYkmBdCCCGEEEIIIZoZCeaFEEIIIYQQQohmRoJ5IYQQooK4uDg0Gg1t27Z1dlOcpm3btmg0Gvtr/Pjxzm5SszRmzBg0Gg3r1q27pOMYjUaH63HHHXfUS/uEEEI0bxLMCyGEaFVsgWpcXJyzm9JgXnjhBTQaDS+88MIlHWf69OnMnDmTSZMm1U/DRJ3MmDGDmTNnMnz4cGc3RQghRBOic3YDhBBCiKYkLCyMQ4cOodfrnd0Up5s7d26r7qHQVHz88ccALFiwgM2bNzu5NUIIIZoKCeaFEEKICvR6PV26dHF2M4QQQgghLki62QshhGgVFixYgEaj4fTp0wC0a9fOYRyybVzzhcbM27YFWLRoEYMGDcLT05M2bdpw8803Ex8fD4CiKHz44Yf06dMHDw8PAgICuOOOO0hNTa22fUePHuW+++4jOjoao9GIt7c3o0aNYtGiRbV6nxqNhhdffBGAF198scHGWu/atYsbb7yR8PBwXF1dMZlMtG/fnunTp/PTTz9Vu8+tt95KZGQkBoMBPz8/Jk2axIoVK6o9T2lpKZ999hnjx48nICAAg8FAeHg448eP54MPPqhy+/nz5zNs2DC8vb0xGo107NiRRx55hMTExCrPUfG6/vDDD4wYMQKTyYSHhwfDhw+/YPvOnDnDXXfdRUhIiP1czz77LAUFBdXuk52dzXPPPUfPnj3x8PDAYDAQGhrK8OHDef755ykpKal2XyGEEMJGMvNCCCFahQ4dOjBz5ky+//578vLymD59Op6envb1wcHBNT7WM888w9y5cxk1ahRXXHEFO3bsYMmSJWzevJm9e/dy//338/PPPzNmzBjat2/P5s2bWbhwIbGxsezcuRNXV1eH43333XfcfvvtFBYW0qVLFyZPnkx2djbbt29nxowZ/Pnnn3z22Wc1atvMmTPZs2cPe/fupXfv3vTp08e+bsSIETV+jxeyZs0arrjiCkpKSujduzdDhw7FYrGQmJjI8uXLsVgsXH311Q77vPfee8yaNQur1UqfPn0YPHgwycnJrFu3jlWrVvHiiy/y/PPPO+yTnZ3NlClT2LRpE3q9nmHDhhEaGkpycjL79u1jzZo1/P3vf7dvX1RUxJQpU/jjjz8wGo2MHTsWk8nEli1b+OCDD1i8eDG///47/fr1q/J9zZkzh5dffplhw4YxefJkDh8+zJYtW5gyZQo//PAD11xzjcP2hw8fZvTo0aSmphISEsJVV11FXl4e77zzDmvXrq3yHPn5+YwYMYK//vqLNm3aMG7cODw8PEhOTrafb9asWfj4+NThygghhGhVFCGEEKIViYqKUgDl1KlTVa4/deqUAihRUVGV1gEKoPj7+yt79uyxL8/Pz1dGjBihAErPnj2V6OhoJS4uzr4+LS1N6dChgwIoixYtcjjmvn37FIPBoBiNRuWHH35wWBcXF6f07NlTAZSFCxfW+D3OmTNHAZQ5c+bUeJ+KLvY9Gjt2bJXvRVEUJSsrS9m6davDspUrVyoajUYJCAhQ1q9f77Bu3759Snh4uAIo69atc1h37bXXKoDSt2/fSm0pKSlRli1b5rDsqaeeUgAlOjraYfvi4mLlb3/7mwIo7dq1U4qKihz2s11XHx8fZdu2bQ7rbN/LTp06VXqvAwcOVADlhhtuUAoKCuzLT58+rURHR9uPu3btWvu6hQsXKoByxRVXKMXFxQ7Hs1gsyrp16yq1z+bzzz9XAGXmzJlVrhdCCNG6SDd7IYQQopZeeuklevfubf/azc2NWbNmAbB//37ef/99oqKi7OsDAgJ44IEHADWrXdG///1vioqKeOWVV7j22msd1kVFRfHpp58C8P777zfIe6mLlJQUACZPnlxpnbe3N0OGDHFYNmfOHBRFYf78+YwaNcphXc+ePXn77bcBHLrN7927l6VLl2I0Gvnll18qDXvQ6XQO2f/CwkLmzZsHwDvvvOOwvV6v5/333ycoKIhTp07x/fffV/m+XnrpJQYPHuyw7JlnnsHb25ujR49y5swZ+/LNmzezc+dOPDw8+OijjzAajfZ1kZGRzJ07t8pz2L53EyZMqFRkUavVMnr06Eo9N4QQQoiqSDAvhBBC1FJVQWzHjh0BNcicOHFiteuTkpLsy6xWK7/99hsAN954Y5XnGjBgAJ6ensTGxlJYWHjJba8PgwYNAuDWW29l06ZNlJaWVrvtuXPn2LFjB25ubkydOrXKbcaMGQPAli1b7MtWrlwJwJVXXklYWNhF2xQTE0Nubi5+fn5Vnsfd3Z2bbroJoNou8FXtZzAYaN++PYDDmHtbjYXLL78cf3//SvtdffXVeHt7V1o+cOBAAN544w2++OILMjIyLvLOhBBCiKpJMC+EEELUUmRkZKVltvH3ISEh6HSVS9J4eXkBOATk6enp5OTkABAREeFQrM720mq15ObmYrVaSU9Pb4i3U2uvvfYa/fr147fffmPkyJGYTCZGjBjBc889x6FDhxy2PXXqFIqiUFBQgMFgqPI9BgYGApCWlmbfz1aosKYzC9gC7Xbt2lW7TXR0tMO256vqugKYTCbA8dolJCRc8HzVFVEcM2YMTz31FKmpqcycOZOAgAA6d+7MXXfdxU8//YTVaq22/UIIIURFUgBPCCGEqCWttvpn4Rdad76KgdvMmTMvur3BYKjxsRtScHAwMTExrF+/nj/++IPNmzezfft2Nm/ezKuvvsprr73GU089BZS/R09PT6ZPn+7MZl9Uba7dpXj99de5//77+eWXX9i0aRObN2/m888/5/PPP2fgwIGsXbsWDw+PRmmLEEKI5kuCeSGEEMJJAgICcHNzo6CggLlz5xIQEODsJtWYRqNhzJgx9i7yhYWFLFiwgIceeoh//vOfXHfddURHRxMREWHf/rPPPqtxwGzLkh8+fLhG29u64p86darabU6ePOmw7aWwHSMuLq7abWy9C6rStm1b/v73v9ur8e/cuZPbbruNnTt38sYbb9inFxRCCCGqI93shRBCtCq24mIXGufdWFxcXJgwYQIA3377bb0d1xnv0Wg0cv/999OrVy+sViv79u0DIDQ0lF69emE2m+3j4Gvi8ssvB2DFihUOdQaqY6stkJGRwc8//1xpfUFBAUuWLAFg7NixNW5HdUaPHg2oY/urGvf+888/k5WVVePjDRw4kAcffBCAPXv2XHL7hBBCtHwSzAshhGhVwsPDAThw4ICTW6KaM2cOrq6uzJ49m4ULF1Y5Zvqvv/5i6dKlNT5mQ7/HuXPnEh8fX2n54cOHOXbsGIBDNf9XXnkFgDvvvJNffvml0n6KorB9+3ZWrVplX9anTx+uvvpqCgoKuPrqqyudr7S01CFoNxqNPPTQQwA8/vjjDlnxkpISHn30UZKTk2nXrh3XXXddXd62g5EjR9KvXz9yc3N56KGHKCoqsq87c+YMTzzxRJX7/fjjj2zYsKHSdS4pKbE/7Kj4vRNCCCGqI93shRBCtCrTp09n7dq13HbbbUycOBFfX18AZs+eTefOnRu9Pf369WPRokXccccd3HHHHTz33HN069aNNm3akJGRwf79+0lISODGG2+sNHVddSZNmoSHhwfLli1jxIgRdOzYERcXF4YPH86dd955yW1+5ZVXmD17Nl26dKFr1664ubmRlJRkr2x/++23069fP/v2U6dO5b333uPxxx/nqquuokOHDnTu3Blvb2/S0tLYu3cvqampPPXUUw4zAXz++edMnjyZbdu20bFjR4YNG0ZoaCjJycns37+ftLQ0FEWxb//iiy8SExPDmjVr6Nq1K2PHjsXLy4utW7cSHx+Pv78/3333Xb1N/fbll18yZswYlixZwoYNGxgxYgT5+fn8+eef9OrVi4CAALZu3eqwz/r163nvvfcICAigb9++BAYGYjab2bZtG6mpqYSFhfHkk0/WS/uEEEK0bBLMCyGEaFUeeOABzGYzixYtYsWKFfYK5bfddptTgnmA66+/noEDB/L++++zevVqNm/ejMViISgoiA4dOvDwww/XKpscFBTEb7/9xksvvcSuXbvYunUrVquV0tLSegnm582bx5o1a9i5cyfr168nLy+P4OBgJkyYwL333usw/7vNI488wmWXXcYHH3zA2rVrWbNmDVqtluDgYPr27cuVV15ZqUCer68v69ev57PPPuPrr79mz549bNmyhcDAQPr06cO0adMctjcYDKxcuZKPP/6YL774go0bN1JUVERERAR///vfeeqpp+plvLxNt27diImJYc6cOfz2228sW7aM8PBw/v73v/P8889XOYXhHXfcgZubG5s2beLgwYOsX78eb29vIiMjeeyxx7j33nurnOpOCCGEOJ9GqfhIWwghhBCtXtu2bTl9+jSnTp2qcno14RwLFizgzjvvZObMmSxYsMDZzRFCCOFkkpkXQgghRJWeeOIJPD096d69O7Nnz3Z2c1qte+65h5KSEo4fP+7spgghhGhCJJgXQgghRJV++OEHAMaNGyfBvBN9+eWXDgX2hBBCCJBu9kIIIYQQQgghRLMjU9MJIYQQQgghhBDNjATzQgghhBBCCCFEMyPBvBBCCCGEEEII0cxIMC+EEEIIIYQQQjQzEswLIYQQQgghhBDNjATzQgghhBBCCCFEMyPBvBBCCCGEEEII0cxIMC+EEEIIIYQQQjQzEswLIYQQrcgdd9yBRqNhwYIFzm7KJbFYLHz//fc888wzTJw4EX9/fzQaDTqdztlNE0IIIRqF/McTQgghRLNjNpu5/vrrnd0MIYQQwmkkmBdCCCFEs6PX67n11lvp27cv/fr1w8/Pjz59+ji7WUIIIUSjkWBeCCGEEM2Oh4cHixYtsn8dFxfnvMYIIYQQTiBj5oUQQohmLj8/n3fffZcRI0bg6+uLwWAgKiqKqVOn8vXXX9foGGazmY8//phrr72Wjh074uHhgYeHBz179uTZZ58lKyuryv3Onj3Lo48+SqdOnTAajbi7uxMREcG4ceOYO3dupe3/+OMPpk6dSlBQEHq9Hl9fXzp27Mhtt93Ghg0bLuXbIIQQQrQqkpkXQgghmrEzZ85w+eWXc/DgQdzd3Rk+fDj+/v4kJiayceNG9u/fzy233HLR4+zdu5d7772XNm3a0LlzZ/r3709mZia7du3i1Vdf5dtvv2Xbtm34+/vb90lOTmbAgAEkJSURGRnJ5ZdfjtFoJCkpiT179rBr1y6eeOIJ+/YLFy7kzjvvBGDQoEGMHTuWgoICEhISWLJkCQEBAYwaNar+v0lCCCFECyTBvBBCCNFMWa1Wrr32Wg4ePMjEiRNZtGgRbdq0sa8vLCzkzz//rNGx2rZtyx9//MHYsWPRass77uXn5/PAAw/wxRdf8PzzzzNv3jz7uv/9738kJSVx7733Mn/+fDQajX1dSUlJpUz7iy++iKIobNy4kREjRjisS01NJTExsVbvXwghhGjNpJu9EEII0Uz98ssvxMTEEBISwg8//OAQyAMYjUYmT55co2OFh4czbtw4h0AewN3dnf/+97/odDq+++47h3UpKSkAXH755Q6BPKgF6saNG1dpe29v70qBPEBgYCB9+/atUVuFEEIIIZl5IYQQotlauXIlALfccguenp71cswtW7awceNG4uPjyc/PR1EUAFxdXUlLSyMzMxNfX19A7Sr/0Ucf8fTTT6MoChMnTrxgOwYNGsS6deu4/fbbefTRR+nbt2+lhwdCCCGEqBkJ5oUQQohm6vTp0wB06dLlko+VmprK9OnT2bRp0wW3y8nJsQfzM2bMYPXq1Xz11VdMnz4dFxcXunXrxogRI7juuuu47LLLHPb96KOPmDJlCl9++SVffvklXl5eDBw4kMsuu4wZM2YQGRl5ye9DCCGEaC3kcbgQQgghuPvuu9m0aRNDhw5l1apVpKSkUFxcjKIoKIpCSEgIgD1TD6DValm0aBEHDhzgjTfeYMqUKZw9e5b//ve/jBs3jquuugqLxWLfvmvXrhw5coTly5fz+OOP06NHDzZu3Mhzzz1Hx44dHaaaE0IIIcSFSTAvhBBCNFO2TPbhw4cv6Th5eXmsWLECrVbLihUrmDBhAoGBgej1evv65OTkavfv1q0bs2fPZtmyZaSmpvLHH38QGBjIL7/8whdffOGwrU6nY/LkycydO5ctW7Zw7tw55syZQ3FxMffddx95eXmX9F6EEEKI1kKCeSGEEKKZuvzyywFYvHjxJQXB2dnZWCwWTCYTPj4+ldYvWrTIISN/IRqNhnHjxtmnw9uzZ88FtzeZTLzwwgv4+PiQn5/P0aNHa9t8IYQQolWSYF4IIYRopq666ir69u1LUlIS119/Penp6Q7rCwsL+e233y56nKCgIHx9fcnKyuLLL790WLdt2zaeeeaZKvf74osv2LVrV6XlZrOZdevWARAVFQWoU9y9/fbbpKWlVdp+48aNZGVl4eLiQnh4+EXbK4QQQggpgCeEEEI0W1qtlh9//JFJkybx22+/ERkZyYgRI/D39ycxMZG9e/fi4+NDXFzcBY/j4uLC888/zz/+8Q9uv/125s2bR/v27YmPj2fLli3cdtttbNiwwV5wz2bp0qXMnDmT0NBQ+vTpg6+vL5mZmWzevJns7Gx69OjBPffcA0BxcTGPP/44s2fPpmfPnnTs2BG9Xk9cXBzbtm0D4Nlnn600vd6FPPjgg+zevRuAoqIiACwWC0OGDLFvc+WVV/Kvf/2rxscUQgghmgsJ5oUQQohmLCoqipiYGD766CO+//57tm7dSnFxMcHBwYwePdre3f1iHnvsMdq1a8cbb7zBwYMHOXDgAF26dGHevHncf//9tGvXrtI+jz/+OO3atWPLli3s3r2bjIwM/Pz86NatG7fccgt33nknHh4eAHh6ejJ//nzWr19PbGwsq1evpri4mNDQUK699loefPDBStXvL+bgwYNs37690vKKy+qj0r8QQgjRFGmUmg6CE0IIIYQQQgghRJMgY+aFEEIIIYQQQohmRoJ5IYQQQgghhBCimZFgXgghhBBCCCGEaGYkmBdCCCGEEEIIIZoZCeaFEEIIIYQQQohmRoJ5IYQQQgghhBCimZF55stYrVaSkpLw8vJCo9E4uzlCCCGEEEIIIVohRVEwm82Ehoai1Vaff5dgvkxSUhIRERHOboYQQgghhBBCCMGZM2cIDw+vdn2TDOY3bNjAm2++ya5duzh79iw//vgj06ZNu+A+69atY9asWRw4cICIiAiee+457rjjjhqf08vLC1C/YSaT6RJaL4QQQgghhBBC1E1OTg4RERH2GLU6TTKYz8vLo3fv3tx1111ce+21F93+1KlTXHnlldx///189dVXrFmzhrvvvpuQkBAmTZpUo3PautabTCYJ5oUQQgghhBBCONXFhn83yWD+iiuu4Iorrqjx9vPnz6ddu3a89dZbAHTt2pVNmzbxzjvv1DiYb07MRbkkmbPoHFB9lwshhBBCCCFaLEUBqXMlWrkWUc1+69atjB8/3mHZpEmT2Lp1a7X7FBUVkZOT4/BqDv69/muGfj2ae379l7ObIoQQoo6KLEXOboIQQjRPigLb/gtvRkPM585ujRBO1SKC+eTkZIKCghyWBQUFkZOTQ0FBQZX7vPbaa3h7e9tfzaX4XVf/aDTaYjLZQ3ZRtrObI4QQopYOnDvA0K+H8kHsB85uihBCNC/5GbDkFlj5NOSnw+Ffnd0iIZyqRQTzdfHMM8+QnZ1tf505c8bZTaqRiR36YSkMBk0pS4+scHZzhBBC1NLGxI2UWEvYmLDR2U0RQojm48wO+L9RUPH+15zsvPYI0QTUSzCfnZ3NH3/8weLFi9myZUt9HLJWgoODSUlJcViWkpKCyWTCzc2tyn0MBoO92F1zKnrnadRjKh0CwE/Hf3Zya4QQQtTW8azjAJzOOY2iKE5ujRBCNAM7P4XPr4DsM+DbDqb9V11uPuvcdgnhZJcUzJvNZu6++24CAwOZNGkSt912G5988ol9/SeffEJoaCjbt2+/5IZeyNChQ1mzZo3DstWrVzN06NAGPa+z9PAZi6JoOGH+i/iceGc3RwghRC0cyzwGQH5pPumF6U5ujRBCNHHFefDbU2Athe7Xwn0boNPl6rr8dCiVGiSi9apzMF9QUMCYMWP47LPP8PX15YorrqiUYZgyZQopKSksW7asVsfOzc1lz5497NmzB1CnntuzZw/x8Wrg+swzz3D77bfbt7///vs5efIkTz75JIcPH+ajjz7i22+/5R//+Edd316T1jc0EkteRwB+OfmLk1sjhBCipootxZzOOW3/uuLnQgghqpC0B6wl4BUK130GRhO4+YKLq7o+N+WCuwvRktU5mH/77beJjY3l5ptv5sSJE/z6a+UCFMHBwXTt2pW1a9fW6tgxMTH07duXvn37AjBr1iz69u3L888/D8DZs2ftgT1Au3btWL58OatXr6Z379689dZbfPLJJy1yWjqAbqEmSrL7AfDLiV+wKlYnt0gIIURNnMo+hUWx2L+W3lVCCHERiTHqx/D+5VPRaTTgFax+LuPmRStW53nmv/nmG4KDg/n0008xGo3VbtepUyd27NhRq2OPGTPmguMIFyxYUOU+sbGxtTpPc9U9xESpuRuKxUBibiK7U3YzIHiAs5slhBDiIo5lHXP4WjLzQghxEQllwXzYefe6XiGQFQ85SY3fJiGaiDpn5k+cOMGgQYMuGMgDuLu7c+7cubqeRlShjZeBAA9PSsw9AelqL4QQzcXxTLX4nV6rByDeLJl5IYS4oMTd6sfw84N5ycwLUedg3sXFhZKSkotul5CQgIeHR11PI6qg0WjoGmKiNEvtav973O8UlhY6uVVCCCEuxpaZHxY6DIC4nDgntkYIIZo4czLkJIBGCyF9HNd5hZRtIxXtRetV52A+OjqavXv3UlpaWu02ubm57Nu3j65du9b1NKIa3UJNWAra4q4JJK8kjz/j/3R2k4QQQlyELTM/LnIcAGdyzkjdEyGEqI6ti32brmDwdFxnD+YlMy9arzoH81dddRVnz57llVdeqXabV155hezsbK655pq6nkZUo1uICdBiLB4IwM8nZc55IYRoynKLc0nKU8d2jgofhYvGhUJLIan5qU5umRBCNFGJu9SP4f0rr5PMvBB1D+b/8Y9/EBYWxssvv8y0adP4+uuvAUhJSWHp0qXcdNNNvPnmm7Rt25b777+/3hosVGowD2lJPQDYmrSVtPw0ZzZJCCHEBRzPUrPyge6B+Lv5E+4VDkhFeyGEqFZiNcXvQMbMC8ElBPM+Pj6sXLmSdu3a8fPPPzNjxgw0Gg0rV67k+uuv59tvvyUyMpJffvlFxsw3gHYBHhh0WvLzfenk0w2rYmXb2W3ObpYQQohq2MbLd/TpCECkVyQAp81S0V4IISqxWiCxbKaqMMnMC1GVOk9NB9CtWzf++usvFixYwIoVKzh58iRWq5WIiAiuuOIK7r33Xtzd3eurraICnYuWLsFe7E3IxkMTAhzkXIHMGiCEEE2Vbbx8B58OAESZotiYuFEy80IIUZVzR6HYDHoPCKyi/pYtM1+UA0W5lcfUC9EKXFIwD2A0Grn//vulK70TdAs1sTchm6Ii9YFJRmGGk1skhBCiOvbMvG9ZZt6kZualor0QQlTBNl4+tC9oXSqvN5rA1ROKcyE3RYJ50SrVuZu9cD7buPnsXAMgwbwQQjRViqJwLFMN5jv4lmXmvaIAGTMvhBBVslWyD+tX/Tb2cfPS1V60ThLMN2PdQsuK4GXrAUgvSHdmc4QQQlQjvTCdrKIsNGiI9o4GyjPzZ8xnsFgtzmyeEEI0Pbbid+FVFL+zkenpRCtX42727du3r/NJNBoNJ06cqPP+omqdg01oNJBlNuLuK5l5IYRoqmxZ+UhTJEadEYAQjxD0Wj0l1hKS85MJ8wxzZhOFEKLpKM6HlIPq51VVsreRzLxo5WoczMfFxVW5XKPRoCjKBddpNJo6NU5cmKdBR1t/D07nqrMFpBdKZl4IIZoiWzBvq2QP4KJ1IcIrgpPZJzmdc1qCeSGEsDm7FxQLeAaDKbT67WzBfI4E86J1qnE3+1OnTlV6zZo1C4Crr76apUuXEhsbS2xsLD/++CPTpk0DYNasWZw8ebJBGi/UcfNKqVrwI6Mwo9oHK0IIIZzHNse8bby8ja2rvYybF0KICip2sb9QUlCmpxOtXI0z81FRUQ5f//zzz7zzzjssWrSIm2++2WFd7969ufrqq1myZAm33norI0aMqLS/qB/dQk0s/0sN5kutpeQU5+Bt8HZyq4QQQlRUVWYeyovgnc6RueaFEMLOXvyuivnlK5Ix86KVq3MBvLlz5zJgwIBKgXxFN910EwMGDOCtt96q62nERXQLMYGiQ2N1A2TcvBBCNDVWxcqJbLVuTHWZeQnmhRCigsTd6scLFb8DycyLVq/Owfy+ffvo2LHjRbfr2LEj+/btq+tpxEXYKtpbSsvGzUtFeyGEaFISzYkUlBbgqnUl0ivSYV2UqWx6OrN0sxdCCAByUyE7HtBASJ8Lb2svgJcMMtRUtEJ1DuatVmuNKtSfOHFCxnE3oEAvA/4erlgrjJsXQgjRdBzLUrvYt/dpj07rOLrNFswnmhMptZY2etuEEKLJsXWxb9MFjKYLb2sL5ksLoDC7YdslRBNU52C+b9++7Nixgx9//LHabZYtW8b27dvp27dvXU8jLkKj0dApyAulLDMvwbwQQjQt1Y2XBwh0D8ToYqRUKSUpN6mxmyaEEE1PUlkX+4uNlwfQu4HRR/1cutqLVqjOwfzs2bNRFIUbbriBW2+9lRUrVnDo0CEOHTrEb7/9xm233cYNN9yARqNh9uzZ9dlmcR4/D1cUi5qZl+nphBCiaamukj2AVqMlwhQBQFxOXGM2Swghmqa0I+rH4B412942dZ0E86IVqnE1+/NNmTKFt956iyeffJIlS5awZMkSh/WKouDi4sJ//vMfpkyZcskNFdUzuenKp6crkMy8EEI0JbbMfAefysE8qBXtj2Uek+nphBACIL1sGK9fdM229wqG1INS0V60SnXOzAP84x//YPfu3dx1111ER0djMBgwGAy0b9+eu+66i5iYGJ544on6aquohpdRb8/MSzd7IYRoOqyKldNmtVJ9tE/VN6ZS0V4IIcpYrZBxUv3cv6bBvFS0F61XnTPzNj179uTjjz+uj7aIOjIZyzPz0s1eCCGajrT8NEqtpbhoXAhyD6pyG6loL4QQZcxJajE7rQ58omq2T8WK9kK0MpeUmRdNg5dRX97NXjLzQgjRZJzNUzNFge6BlSrZ29iCecnMCyFaPVsXe9+24FLDnKNk5kUrJsF8C2By02G1FcCTeeaFEKLJsAXzIR4h1W5jC+bP5p2l2FLcKO0SQogmKV0tGIp/1TVGqmTLzOdIMC9anzp3s7/ssstqvK1Go2HNmjV1PZW4CC+D3j41XW5JLkWWIgwuBie3SgghhG26uVDP0Gq38Tf6465zJ780nwRzAu192jdW84QQommpbfE7AC9bNXvpZi9anzoH8+vWrbvoNhqNBkVR0Gg0dT2NqAGTmx6sbqC4gMZCZmEmwR7Bzm6WEEK0ejXJzGs0GiJNkRzOOExCrgTzQohWLKMsmK9p8Tsoz8znJqsF9LTS8Vi0HnUO5teuXVvlcqvVyunTp/n1119ZunQpzzzzDBMnTqxzA8XFeRl1gAYsnqDLJr0gXYJ5IYRoAmqSmQfwd/MHpO6JEKKVq0s3e89AQAPWUshPB882DdI0IZqiOgfzo0ePvuD6O+64g/fff58nn3ySG264oa6nETVgctMDYC31QKvLlor2QgjRRNgy86EeFwnmjRLMCyFaOUspZMapn9cmM++iB482kJeqFsGTYF60Ig3aD+WRRx4hIiKCF154oSFP0+qpmXmwSkV7IYRoMhRFsWfmQzyr72YP4Gf0A6SIqRCiFcs6rWbXdW7l4+Bryj49nRTBE61Lgw8q6d27N5s2bWro07Rqnq46NBrK55qXm0EhhHC6nOIc8kvzgQuPmYfyYF4exgohWq30CuPlazvuXaanE61UgwfzGRkZ5ObmNvRpWjWtVoOnQYdikcy8EEI0FbasvJ/RD6POeMFtZcy8EKLVsxW/86tDEVCTLZiXivaidWnQYH7Dhg1s3LiR6OhajHsRdWIy6qWbvRBCNCFJeWXF7y4yXh4kMy+EEHUqfmcjmXnRStW5AN5LL71U7Tqz2cyhQ4f4/fffsVqt3H333XU9jaghL6OOlAJ1rnnpZi+EEM53NrdsWrqLjJcHGTMvhBCXFszbxsxLZl60LnUO5l944QX7PPLV0Wq1PProozz22GN1PY2oIZObHsUsmXkhhGgqbJn5i42Xh/JgPrMwE6tiRauReZKFEK1M+kn1Y20q2dtIZl60UnUO5p9//nk0Gk2V61xdXQkLC+Oyyy4jPDy8zo0TNWcy6uwF8CSYF0II57Nl5i82xzyUB/OlSinmYjPeBu8GbZsQQjQpJQWQfUb9/FIy8zkSzIvW5ZIy86LpMBn1DgXwJLMjhBDOVZvMvKuLK16uXpiLzaQXpkswL4RoXTJOAQoYvMHdv/b726ayy0sDS4k697wQrUCdo734+HgyMi6eAc7MzCQ+Pr6upxE15GXUoZSqY+YtioWcohwnt0gIIVq32mTmAfyNZRXtC6R3lRCilcmoMC1dNT1/L8jdH7Q6QIHc1HptmhBNWZ2D+Xbt2jF79uyLbvfkk0/Svn0dppgQtWJy0wM69Bo1oJeu9kII4TwFpQVkFmUCNcvMQ4UieIVSBE8I0cpcSvE7UOel97QVwZOu9qL1qHMwryjKBYvfnb+taFheRnXEhB4TIDeDQgjhTGfz1JtJD70HJldTjfaR6emEEK1WeoXMfF15h6kfsxMuvT1CNBMNPqjabDbj6ura0Kdp9UxGdWyQi+IF1DGYt1rg4M9QKF30hRDiUtinpfMIqbZY7Pn83cq62UswL4RobezBfB0z8wDeZUW3JZgXrUiDBfNWq5X9+/fz559/EhkZ2VCnEWW8yoJ5bEXw6jLmcsNc+HYGrLj48AkhhBDVsxW/q+l4eaiQmZcx80KI1sbezf5SMvMSzIvWp1bBvIuLi/0FsHDhQodlFV96vZ4+ffqQnp7Otdde2yCNF+VMbmo3e2uJGszXOjNfUgA7/k/9/OAyKMiqv8YJIUQrUzEzX1MyZl4I0SoV5kBeWdE6v0sJ5iPUj7Yp7oRoBWoVzNvGySuKgkajcfj6/JdOp6Nt27Y8/vjjvPjiiw3VflHGlpkvLXEH6tBNc+9iyC+7gSwthANL67N5QgjRqlxSZl662QshWhNbJXuPQDDWrMZIlSQzL1qhWgXzVqvV/lIUhTvuuMNhWcVXUVERJ06c4I033qjzmPl58+bRtm1bjEYjgwcPZseOHdVuu2DBAjQajcPLaDTW6bzNkamsAF5RUVkwX5tumlYrbP1I/bxNF/Xjnq/rs3lCCNGq2Kel86h5MC9j5oUQrVJ9FL8DCeZFq1TnMfNz5sxh2rRp9dgUR9988w2zZs1izpw57N69m969ezNp0iRSU6ufO9JkMnH27Fn76/Tp0w3WvqbGlpkvKFSD+Vp10zy2CtKPgcEbbvoaNC6QsBPSjtatMVYr7PwEYj4Dc3LdjiGEEM2YLTMf4ln7bvYyZl4I0arUdzCff04dPipEK6Cr645z5sypz3ZU8vbbb3PPPfdw5513AjB//nyWL1/OZ599xtNPP13lPhqNhuDg4AZtV1Nlm5pOKS0rgFebzM7WD9WP/Weqf0g7ToCjK2HPVzChDkMkts2DVc+pn/86CyIGQZcpEH0ZFOeBOQlyktR5QEP7Qo/ptT+HEEI0USXWElLz1QfPtcnM24J5c4mZYksxri4yE4wQohW41DnmbYw+4OoJxbmQnQgBl3g8IZqBOgfzDam4uJhdu3bxzDPP2JdptVrGjx/P1q1bq90vNzeXqKgorFYr/fr149VXX6V79+5VbltUVERRUZH965yc5j0dm1HvgqtOS0ltg/mzeyFuI2h1MPg+dVmfW9Vgft83MO550LrUvCFJsfBH2QOAgM5w7gic2a6+Vv+rih00EDkMTDXPXgkhRFOWmp+KVbGi1+rtXedrwuRqQqfVUWotJaMwg2CP1vlwWgjRytjGzF9K8TsAjUbNzqcdVovgSTAvWoEaB/OXXXYZGo2GhQsXEh4ezmWXXVbjk2g0GtasWVPj7c+dO4fFYiEoKMhheVBQEIcPH65yn86dO/PZZ5/Rq1cvsrOzmTt3LsOGDePAgQOEh4dX2v61115rcYX5TEY95/LVYD6vJI/C0kKMuovUDdhSlpXvfk1596ROl4Obn5o5P7EWOo6vWQOKcuH7v4G1BLpOhRu+VI9xeDkc+hkSd4O7H5jCwCsEEmIgOx6OrICBf6vjuxZCiKYlKbesi71HCFpNzUezaTQa/Ix+pOankl6YLsG8EKLlU5T6y8yDWtE+7bCMmxetRo2D+XXr1qHRaMjPz7d/XVMajabWDautoUOHMnToUPvXw4YNo2vXrvzf//0fL7/8cqXtn3nmGWbNmmX/Oicnh4iIiAZvZ0MyGXWcyzWg0+gpVUrIKMy4cCXl7MTyqvVDHypfrnOFnterU9XtWVTzYP63J9Wnq6YwmPq++oTUFAqD7lFf59v4Nqx5UQ32JZgXQrQQZ/PKpqWrxXh5G3+jP6n5qTJuXgjROuSnQ2E2oAG/dpd+PHsRPJmeTrQONQ7m165dC0BkZKTD1w0hICAAFxcXUlJSHJanpKTUeEy8Xq+nb9++HD9+vMr1BoMBg8FwyW1tSrzc9IAGT70PWcVpFw/md/wfWEshaoQ6dr2ivreq6w8vh4JMcPO98Mn3f6+Osddo4dqP1Qz8xXSdqgbzpzaof8iN3hffRwghmriKmfnakunphBCtSsoB9aNvFOjdLv14UtFetDI1DuZHjx59wa/rk6urK/3792fNmjX2ivlWq5U1a9bw8MMP1+gYFouF/fv3M3ny5AZrZ1Njm57O3cWHLNJIL7hARfuiXNi1QP18WBXf0+BeENQDUv6Cv36AgXdXf6yMU/DrP9TPRz4BbYfXrMEBHSGgE5w7CsdWQ8/rarafEEI0YbbMfG2K39nYgvlazUgihBDNVcpf6sfgnvVzPO+yXraSmRetRJ2npmtos2bN4uOPP2bhwoUcOnSIBx54gLy8PHt1+9tvv92hQN5LL73EqlWrOHnyJLt37+a2227j9OnT3H33BYLQFsZUNj2dQaNmuC+Y2dn3jZoN92sPHSdVXq/RQJ9b1M93f1n1FB9pR2D54/Df4VCUAxGDYfRTtWt0lyvVj4d/rd1+QgjRRNkz83XoZi/T0wkhWpXk/erHoPoK5iUzL1qXJlnNHuDGG28kLS2N559/nuTkZPr06cPKlSvtRfHi4+PRasufRWRmZnLPPfeQnJyMr68v/fv3Z8uWLXTr1s1Zb6HR2aan0+MFXCCzoyiw43/q54PuBW01z3R63gCrn4eze+DVMAjqDuEDoE0Xtfv9qfXl2wb1gOmfgEstf6S6TIFN76iZ+dIi0LWsoQ9CiNYnOS8ZqFtm3lb9XrrZCyFahWRbZr5H/RzPHswngtVa/T2uEC1EjSOvl156qc4n0Wg0/OtfVU1LdmEPP/xwtd3qzy/A98477/DOO+/UpXkthslNzcy7KGXBfHXd7E+tVyt9unqWZ9+r4tkGxj4L2/4LeamQvE992Wi00Hmy+kCg3Sg1m19bof3Uyvbms+rY+Y4Tan8MIYRoIhRFuaQCeDJmXgjRapQWq/ejoCaF6oMpFNCApQjyz4FnYP0cV4gmqsbB/AsvvIBGo0FRlBof3LZ9XYN5UTtehrLLaVGD+WpvBrf/n/qx980XLzo3chaM+IfaXSlxl/pKPahm6Qf8TS1Ycim0ZQ8EYj6FQ79IMC+EaNbSC9MpshShQUOwe+2nlpMx80KIVuPcUXU6Y4M3+ETWzzFd9GVJoiR13LwE86KFq3EwP2fOnIZsh6gHtsy8tdQDqOZmMDMOjvymfj7o3podWKMBnwj11X3apTf0fF2uVIP5IyvA+g5oXer/HE5msSokZOYT6ede7VSNpRYrGXnFtPEyNMp0jkKI+nc2V83Kt3Fvg95FX+v9/dxkzLwQopVIqdDFvj7ve7zDy4L5BAjrX3/HFaIJkmC+BbGNmS8p8QCXajLzOz4GFIi+DNp0atwGVqftSPWpbF4aJMRA5GBnt6hepZoLuf/LXeyOzyLU28jE7sFM6h7MwLa+lFoVNh07x+8HkvnjUAqZ+SUMbe/Pq9f2pF2Ah7ObLoSopaQ8tfhdXcbLgzrPPKh/v20924QQokWyF7+rpy72Nt7hkLBDiuCJVqHJFsATtWerZl9U5AHuVYyZL86D2C/Vzwfd18ituwCdK3SaCPu/U6vat6Bg/q/EbO75Ioaz2YUAJGUXsmBLHAu2xOHrrqeo1Ep+scVhn60n05n07gYeHdeRe0e1R+8ixVuEaC5smfm6jJeH8m72pUopOcU5eBsuMhRKCCGaK1swX1/F72ykor1oReotmFcUhfT0dBRFwd/f36HSvGgctsx8Yb4XuKuZnbySPDz0ZRle23R0vu2g40QntrQKXa4sD+YnvFS/3a2cZPm+szz+3R4KS6y0b+PBvFv6kZBZ4JCFBwjxNjKpezATuwcRbDIy5+cDbDx2jjd/P8Ive5N4+LIOuLuWDz1w0WrpH+WLp0GexQnR1MTlxAEQ4RVRp/1dXVzx0nthLjGTUZghwbwQomVSlPJu9vWemZe55kXrccnRwOrVq3nrrbfYtGkTBQXqXORGo5GRI0fyj3/8g0mTqpjDXDQI25j53AJXfA2+ZBZlEp8TT1f/ruofze226ejuaXpTdXQYDy6ukHFSrWwa2NXZLaqTwhILx1JyWb7/LPPXnwBgVKc2fHBzX7zd9HQNMTGhWxClFit7zmRh0LnQI8zk0JX2i7sGsWxPIi//eojDyWYe/jq20nl83PXcO6o9M4e2xUOCeiGajONZxwHo4NOhzsfwc/PDXGImvSCddt7t6qtpQgjRdJiTIT9dnRmpvu/5fGzBvGTmRct3SVHA7NmzefvttytVuC8oKGDVqlWsXr2axx57jLfeeuuSGilqxpaZzyksoYspksy0TOLNZcF83EZIOwR6d+hzq5NbWgWDF7QfA8dWqVXtm0EwrygKJ9Ly2Hoyndj4TA4m5XA8NZdSa/nvw99GtOOZK7qgO6+rvM5Fy4C2flUeV6PRcE3fcEZ3CuTt1UfYn5DtsP5cbjGJWQW8sfIIn2w8xf2j2zNjSFvcXFte4cCWQFEU/jiUSnxGPjOGROGqa2IP0kS9URSFk1knAYj2ia7zcfyN/pzOOS3T0wkhWi5bVj6gE+jd6vfYtm72WZKZFy1fnYP5RYsW8dZbb+Hm5saDDz7IzJkzaddOzSDExcWxcOFCPvroI95991369u3LbbfdVm+NFlWzZeaLS62Ee0awN20v8Tnx6srYr9SPvW8CNx/nNPBiul2tBvP7voFRs5tkV/u8olKW7zvLpuPn2HYynVRzUaVtfN31dAs1ccOACK7uE1bnc/l5uPLKtJ6VllusCj/vTeS9P44Rl57PqysO8+Gfxxka7c+w6ACGRvvTMdBTCmc5maIo/Hk4lbdXH+VAUg4A206m89Gt/aQOQguVmp+KucSMi8aFtqa2dT6OzDUvhGjxkvepH+u7iz2UB/P556CkoP4fFgjRhNQ5mP/ggw9wcXFh5cqVjBw50mFd9+7deeONN7jqqqsYM2YMH374oQTzjcDTVYdGo/aoDzSqf8hO55wGq0UNkgF6THdiCy+i29WwYjakH4eEnRAxyNktskvKKmDhlji+3hGPubDUvtxVp6VfpA+D2vnTM8yb7qEmQryNDRpIu2jVzP3UXqH8GJvI+38e40xGAb8fSOH3AykABHi6cvfI9tw3qn2TCOpzi0pJzi6kfYAHWq3z29OQFEVh/dE03ll9lL1lvSo8XF0osSqsPpjCI4tjef/mvhLQt0AnstShNZGmSFxdXOt8HAnmhRAtXnKFaenqm9EHXD2hOBeyEyGg7sOehGjq6hzM//XXX4wYMaJSIF+Rbf3OnTvrehpRC1qtBk+DDnNhKX4GdVqkeHM8JO6CggwwekNEE64Ub/BSA/q9i2HPV40ezB9NMbNgSxyKAiY3Hd5ueryMenaeymD5/rNYyrrPtwvwYGqvEIZE+9Mv0hej3jnd23UuWq4fEME1fcPYm5DNtpPpbD2RTszpDM7lFvP6b4dJMxfx3JVdGzSgP5ORz7FUMx0DvQj3dbOfS1EUtp/K4NuYM/y2P5mCEguBXgYmdg/i8u4hDG7v16IC2qJSC7/sPcsnG09yONkMgJvehZnD2nLvqPbsTcjivi928dtfyTz2zR7eu7FPpeEXNtn5JZzJzCchM59zucXkFJaQU1BKTmEJBcUWAk0Gwn3dCfd1I8LXnQg/Nww6GWbhbPUxXh7K55qvNCOJEEK0FPbid5V7IF4yjUbNzqcdVovgSTAvWrA6B/NGo5HQ0IvPoxsaGoqra90zFKJ2TEY95sJSvHXqtTmdcxqO/q6ujB4HLnontq4G+tyiBvN/LYVJr4Gre6OcdvXBFB5bEkveedPEVTS0vT9/G9GOy7oENqnsss5FrW7fP8qXh8Z2oKjUwpdbT/PK8kN8uukU+cUW/j2tR723uajUwry1J/jvuuOUWNQHHV5GHd1CTLRv48nm4+eIz8gvb6dWQ6q5iEXb4lm0LR5vNz19InyI8FMD0nBfd6IDPegc5NUkehOA2iNDAUKr6W2hKApJ2YX8uDuBhVtPk1Y27MJN78KtgyO5f0w0AZ4GAMZ2DuS/t/Xj/kW7WL7vLC4aDXOmduNwspmDSTkcSMrmaEouZzLzHXp/1IS7qwujO7Xh8h7BjO0SaJ+mUjSuE9lqZv5SxsuD41zzQgjR4hTnq70woWEy81AhmJcieKJlq3Mw379/f/bt23fR7fbt28eAAQPqehpRS7YieB7aIEC9Gcw9uhJPgE7NYGaBqBHgHQnZ8XB4OfS6vt4OrShKpYBMURQ+WneCuauOoCgwuJ0fw6IDyCksIbughJyCEvw9Xbl1cBQ9wprHFFEGnQt3j2yPyajnqaX7WLwjnsISC29e16vaTHB1FEXBqqhd+yvacSqDp5fu42RaHgBR/u6czSrEXFjK9lMZbD+lBiEeri5M7R3K9QMi6BFmYsuJdFYdSGb1wRTO5Raz/mhapXNO7R3Ka9f2rHLqvaMpZpbFJtI2wIPxXYPw86jdg8Lk7EK2njxX1oMhE6POhe6hJvUV5k2Ql5Fd8RlsPZHO1pPpnMlQZ+jwdtPTLUTdrl0bD+LT8zmQlMPBszlk5BXbjx9sMnLH8LbcPDASb/fKAfW4rkHMu6UfD361m5/3JvHz3qRq2xrg6Uq4rzttvAx4u+nxdtNjMuox6LWk5BRyJqOAhMx8EjILyC0q5be/kvntr2T0LhqGRgfw9OVd6BZqqtX3R1waW2b+UoN56WYvhGjRUg+BYgWPNuAZ1DDnkLnmRStR52D+2WefZdy4cbzxxhs8+eSTVW7z5ptvcujQIT744IM6N1DUji0jV1JiwM/oR0ZhBvGZR+iGRp3+ranTaqHPzbD+P2pX+0sM5q1WhTWHU/lk40l2nc6ka4iJodH+DG3vT69wb1769SA/7VEDqtuHRvGvKd1aTNfvGwZGYHR1YdY3e/gxNpHM/GKGtPd32KaNp4FuoSY6BHra33dhiYXNx8/x+4Fk/jiUSk5BCSE+xrLsuRtFpVb796yNl4EXr+rOFT2CKbEoHE/N5UBSNsfTcukU6MUVPYNxdy3/MzO2cyBjOwfyyjSFPWeyOJZiJiGzoKxLeQF7z2Txy94kDiRl899b+9M52MvepnlrjzN//Ql7LwAXrYZBbf2Y1D2I3hE+5BVZyrqjlz2IKeuabvv8dHo+p87lVfo+HTybw3e7qv4eumg1aIDsghK2nlQD/Kq26RXuzR3D2jK5Z8hFf34mdg/mw1v68vfFsZRYFCL93O0PCrqGmIjyV3sp1HR2AkVR+Csxh98PJPP7gWSOpeay4WgaRp2W/90uD1Ibi0Mle28J5oUQolop+9WPQT0artixBPOilahzMK/RaHj44Yd55pln+O6775gxY4a9mv2pU6dYtGgRu3bt4pFHHkGr1bJhwwaH/UeNGnVpLRdVMrmpl9RcWEKUKUoN5vV6ugX2BY8AJ7euhnqXBfMn16l/hG1/kGshv7iUH3Yl8NnmOIcAbn9iNvsTs/nfhpP2ZTqthheu6s5tQ6Lqo/VNylW9Q3HTu/DQV7tZdySNdUcqZ8JBLeTXOciLNl4Gtp9MrzTc4ExGgT1LbXPzoEievqIL3mWzKLjqNHQLNdUoG+yi1diHBlS063QGD30Vy8m0PK6et4l/T+tJmK8b/1y6n5Nl13FEhwAy8oo5eDan2gC7OloN9AjzZmh7fwa396O4VOFgUjYHknI4kJRDqrnQvn5ItD8D2/qhd9FwLCWXg2WZ+FPn8oj0c6d72XvtFORV67oJl/cIYeszfrjqtJfcJV6j0dAz3Jue4d48MakzP+9N4pHFscSlV35wIRpOSn4KuSW56DS6S6pkDxXGzBfKmHkhRAvUkMXvbLxtc83L9HSiZatzMD9mzBg0Gg2KorBr1y52797tsN429/z777/P+++/77BOo9FQWlq7MaGiZrzKAoOcwhIivCKITY3ltF4HnSY6uWW14NdO7W5/epM6fn7UbPuqwhILqw6moAEi/NRMsb+HK1YF/krMVoO7E+nsjMsgvywgNRl13DI4iqt6h3I0xWzvQh2fkY+Pu56Pbu3HsOhm8qCjDiZ0C2LxvUP4ftcZe1YbwKooJGQWcCgpB3NRKfsTy+ezDzYZmdQ9iEndg2kb4EFCptql+0xGAZn5xUzuGcKgdn713tb+UX4sf2QEj32zh43HzvH4d3vt69p4GXjpqu5c3iMYjUZDfHo+qw6q2eiEzAJMRj0mNx0mY1mXdDc9JqNO/eimp42XgX6RvvaHDzaX9wi2f26xKpWGFID6AKC+h1nYxtLXt15l7YzPyK9yaIloGLYu9pGmSPSXWJvENmbeXGym2FJ8SZXxhRCiyWnI4nc2kpkXrUSdg/lRo0bJTWITZDLaMvOlRLVR5ziP1+mgY9MfL2+xKuxPzKZTkCfufW5Rg/k9X8PIJ0CjYfvJdJ75cb99nLaNm94FF62G3CLHB0RR/u7cNbwd1/UPx6Ns/HW3UBPT+qrfl7PZBbjrdVWObW5pqsqC21itCmcy1THgSVkFDGjrR68wb4eCeaE+bg0SvFfF39PAgjsH8eGfx3l3zVEUBW4ZHMlTl3dxCMQj/d25e2R77h7Zvt7OXVUg39yE+rih1UBhiZU0cxGBJqOzm9Qq2Kalu9Tx8gAmVxM6jY5SpZSMwgyCPYIvvpMQQjQHVmsjZeYrBPOK0nDd+YVwsjoH8+vWravHZoj6Ys/MF5TQvVANek8bPSC4AZ9+1oNzuUU8tmQPm46fw9Og49ru0czRueOScZLcY5t5Zb+JJTvVrlIBngbaBbhzJqOAFHMhBSVqBt7LqGNwOz+GtPdnaLQ/XYNNF6zgHuLt1ijvranTajVE+XsQ5e/h7KbYuWg1PDq+IxO6qYVxpJBbzbnqtIT6uJGQWUB8Rr4E842kvqalA7X3mp/Rj9SCVAnmhRAtS9ZpKDaDiysEdGq483iFAhqwFEHeOfBs03DnEsKJ6hzMi6apfMx8KVGp6s3lGVdXpz2RLC61cizVzIGkHNLMRYzu1IbuoSaHXh074zJ4+OvdpOSo03rlFpXyxe50euoGcL1uA38sfpslBX8DKo/TLiq1kJRVSFGphY6BXi0isyrKSRBfN5F+7iRkFnA6PZ8BbRunR0VrV5+ZeVDHzduCeSGEaDFsXezbdGnY6ZJ1ruAVAuYkddy8BPOihZJgvoUpz8wXE3l2C/hChlKCudiMl6tXrY93JiOfE2m5DG7nX+Pq2sdSzHy57TQxcZkcSzU7jNN+8/cjdAn24oYBEVzdJ5TvdyXwxu9HsFgVOgR68tGt/cjIK+a7mAR+2T+W69nAeOtmugXcw5xrBzH4vGrsBp0L7QKaTkZZiKYg0s+dLSfUuhCi4SmKYg/m6yMzD+UV7dMLpAieEKIFsXexb4Qeo97hZcF8AoT1a/jzCeEElxzMFxYWEhMTQ1JSEoWFhdVud/vtt1/qqUQN2Cpje+WexCMrHn9TGOkuLsTnxNM9oHutjpWRV8w1H23mXG4xRr2W0Z3acHmPYC7rElSpiJiiKGw6fo5PNp6qNHe4l1FH91ATXkY964+kcTjZzEu/HuTl5Qcpq5PI1X1CefWanvax7UPa+2Oe2gXzhwvwyjvNz0OOomt/RR2/K6JZSjkAq/4F5mQI6wth/SFsAAR2g9JCMJ+FnETIOav2PAntC/4d1ekNW7lIf3dAfRgnGt7ZvLPkl+aj0+qINEXWyzFtRfAkMy+EaFHit6ofgxpwvLyNdzgk7JCK9qJFu6Rg/s033+TVV18lJyfnottKMN84vMoK4HXP3QZAlIsH6RRyOud0rYP5l345wLncYly0GgpLrPx+IIXfD6Sg02oIMhnt1cK93fScTs/nSIoZUOOqSd2CubpPKD3CvAn3dbN3q8/KL+bnvUl8F5PA/sRsXF20PD+1G7cOjqxUUNHLzQDjZsPPD6Pb+gEMugdc3S/1WySaupJC2PAmbH4XrGVFDVMPQOwi9XONCyiWqvc1mNSgPnwAdJnSap/ER/qpvyenJZhvFLbx8m1NbdFr66fbqMw1L4RocdKOwqn1gAa6TG7480lFe9EK1DmY//DDD3nqqacA6NmzJx07dsTLq/bduEX9MpVlzPsV7QAg0rsdu7MPcdp8ulbH+eNgCsv2JKHVwA8PDEOn1bDqQDIrDyRzNCWXxKwCErMc5x33cHXh+gER3Dm8bbXF1HzcXbl9aFtuH9qW46lmDDoXIvwuEKD3vgk2vAFZ8bDrcxj6UK3eh2hm4jbBL49Cuhoc0WUK9LoRzu6FxF2QuBuKyqbQc/UCUwiYQtUHAGf3QlGOeqNwaj1sfAvCB8Kg+6Db1er4uVYiyk/9/ZNu9o2jvsfLQ/lc8xLMCyFajB3/Uz92vgJ82zb8+WSuedEKXFIwr9Pp+OGHH5g6dWp9tklcAi+jDh/M9FIOARAVOhCyDxGfE1/jY2QXlPDssv0A3DOyPX0ifAB1ru1ZEzuTmFVAmrmI7IIScgpKyCksQa/VMqlHcKXu9xfSIbAGD39c9DDycTXA2/weDLgL9FKFvkWwlELqwbIgPUYN1FMPqus8g2Hym9DtKvVr20erVR3/ZjCB0VT98eI2wsGfIWGn+lr1LAy8G4Y/1iqCeltmPs1cRH5xKe6uUh6lIdky8/UazMuYeSFES1KYA3sXq58Pvq9xzulTFsynHpLp6Vqy0mJI2a/eR/pEQqemPx13farzHV5cXByjRo2SQL6JMRn13ObyBzqsKME9iQzsDYcg3lzzYP7V5YdIySmiXYAH/5hQedqQMB83wnwaMaDufQtsmKs+Wd21EIbc33jnFg0jfht8d4c67v18/e+A8S+Cm0/ldVptebe587noIKSX+hpwJ+Smwq4FEPOZep61/4aEGLjhC9C37OnavN31eLvpyS4o4UxGAZ2DpddUQ6rv4ndQPmY+vVCCeSFEC7DnayjOVavYtxvdOOeMGgYuBrW3X8pfTX6aZlELGSdhx8dqwubsPnUKQgCNFv5xQO212UrUuVJUYGAgbdrINA9NjZdLMXfqVgJQMPAhokxRADXOzG88lsY3MWfQaOCN63ph1Nesgn2D0rnCyFnq55vfVbtUi+Zr33ewcKoaYBtM0H6M2vvipq/h8aMw9b2qA/na8gyE0U/CY/th2nzQucGx3+Hr66Eo99KP38TZx82n5zm5JS2bVbFyMvskUL+Zedvf7hNZJygoLbjI1kII0YRZrbDj/9TPB93beBlyozd0nKB+vv/7xjmnaFiWEtj0Dnw0FLZ9pAbzliJw81WHXypWtYdmK1LnYP6KK65g69atWK3W+myPuETG/V/jrzETb21DZrspRHipXYyyirLIto01rkZuUSlP/6B2r799SBQDm9L81H1uBVO4GgDGfuns1oi6UBRY9zosvRssxep4+McPw+0/wbjnocuV4BVU/+d10UOfm+G2H8DVE05tgEXXQuGFfx+aO1swL+PmG1ZSbhIFpQXotXoiveqnkj1AhFcEQe5BlFhL2JO6p96OK4QQje7EGjWTavBW6+A0pp7Xqx//Wop9CiXRPCXuhv+NhT9eUGc1ajcKrv0Y/r4bnjwF3aep2yXtcWIjG1+dg/k5c+ZQXFzMI488QnFxcX22SdSVpQS2fADA/yxTyClScNe708ZN7UFxoex8cnYht3y8jcSsAsJ83Hjy8i6N0uQa0xlgxGPq55vegdIipzZH1FJJISy9B9a9pn49/FG44UtwrbpQYoNoO1x9cGD0hjPbYeFVkN9yi4vZpqeTYL5h2brYt/Vui05bf7UJNBoNg4IHAbAzeWe9HVcIcR5FgZ2fwJ//VoMFCfjq3/b56se+t4HBs3HP3WmSmrHNjoczOxr33KJ+ZJ6GFU/CJ+PUsfFGH7j6I7j9Z+h1A/hHl01R3Efd/uweJza28dX5ziM0NJRNmzZx1VVX0blzZ8aOHUtkZCTaKuZ41mg0/Otf/7qkhooa+OsHyD5Dhsab7yyjuapQndYr0hRJWkEap82n6dmm8nihPWeyuPeLGFLNRfi66/nwlr72+d6blH63w8a31bnFd3+hTlUnmjZFgYM/wZ8vq2PWtDq48i11XLwzhA+Amb/Cl9PUP/b/GwNT34Xoy5zTngYkmfnGYSt+18G7/sbL2wwKGcQvJ39he/L2ej+2EK1GQZZaOFdnqHr9utdh/evq5xveUHsBdp2i9hYL7Abu/lI47VKcOw7H/wA0MOjuxj+/3k29lvuWwF/fQ+Tgxm+DqD1FUWcm2v4/OPqb2n0eoMd1cPnr4FnFUO+QvurHpNhWVfCwzhGboii89957HD58GKvVyoIFCypto9FoUBRFgvnGYLWqGWvgV7dpFBW4klNQAqhjL3el7KoyM//TnkRmf7+P4lIrnYO8+GTmgAtPFedMOgOM+Af8Nht+fxbadFa72Iim6cRaWPOi+kcV1Bui6Z9C9FjntiukF9yxAr66DrJOw5fXQO+bYdKr4N6EhpZcoigJ5htFQ0xLZ2PLzB84d4C8kjw89I3Yk0WI5k5R1CKoK58GNz+47lO1IFpFW+eVB/LtRkHCLshJUDPJtmyyiwG8gtWCWm26QP+ZENq3Ud9Ks7bzY/Vjp0ng1945beh5nRrMH/gRJr2mFswVTVNRrnqtdnwMaYfLl7cfo/bqvFDyJai7mjTKT4fshPLZDFq4Ov80v/nmm3zwwQfodDqmTJlCx44d8fRs5K4zotzRleoPvcHEJt9pkFGAuUgN5m3jOM+vaP/+mmO8vfooAOO7BvLuTX3xbIoZ+YoG3KU+qTv8Kyy+We02HT7A2a0SFWWeVqcSPLlW/drVE4Y+DEMfqjydnLMEdoEHt8Kal9V5b/cuhmOr1Ke9Pa9vEU9zbQ/lEjIKsFgVXLTN/z01RfbMfD1WsrcJ9Qwl3DOchNwEdqXsYlS4PLwUokYKc9T/QweWql+bk2DBFBj7TxgxS50ZZfeX8Ps/1fVjn4PRs6GkQH0QffhXOL4GcpPV4lpZp9VX/FbY9TmED1KnV+t6VauY7rTOCrMh9iv188aajq4q7ceoD3Ty0tR7yA7jnNcWUbX0E+pwl9ivwFbjy9VTTbYMukdN4F2M3ghtuqpd8c/ukWD+Yj755BPc3d3ZuHEjffvKE0qnUhTY9Lb6+cC/oUvxBgrIKVC72VdV0T4lp9AeyD8wJprZEzujbQ43+y46uO4z+PoGOLkOFk2HO1eoT+OE8x38GX56WP1DrNXDwL/ByCeq7g7lbAYvmPyGGrz/8og6R/3Se9QCeVe+3exv0EJ93NBpNRRbrKTkFBLamNNJthIWq4VT2aeAhsnMg9rVPuFYAjuTd0owL0RNJO1Rpz7NPKVm6S57Tp1nfN836pCvuE3Q7SpY/ri6/dCHYdQT6ud6N+gyWX2BWp/HnKwW381JhCMr1exuwg715RlUOchw84NRsyG4R2O946brt6eh2AwBnaG9E3vluejV4mgxn6lDUiWYd778DEjarfaGOb1Zfchi4xetznrQ52a1zlFthPZRg/mkPdC1dUyfXudg/syZM4wZM0YC+abg9GZ1agYXAwx+ANOqFADMhWpm3lbR/nTOafsu64+kAdA7woenmlqxu4vRGdRpzL6Ypv4z/WIa3LVSLYAhGpbVqs7V6uarzvduy2CXFsGq59QsN0DYAJj+sfO61NVGxEC4d7067eG619TZEjLj4MYv1ffZTLloNYT7uhGXns/p9HwJ5hvA5qTNFFoK8dR7Eu4V3iDnGBQ8iKXHlrL9rIybF8JBZhysfl4Ntm0URc3IWYrBO1J9+B8xUF3ebhQsf0LtNWbrOdZvJkx8pfreWDoD+EapL4Ae09Xtdy1QA8PcZMhNqbzf4V9h2CPq9Kj6Vvq3d993sPdrdd7vqe86v8dbz+vVa3boF/WBvd7o3Pa0JiWF6r1jQow6bVziLsg4UXm7jhNh0H1qV/oqarDVSGgf9T6uFRXBq3MwHxwcjJeXV322RdRF+gn4+RH18763gVcQXsZ0AHIqFMADyCnOIaswCx+jD+uOpgIwppOaMVUUhY2JG1kTv4YbO99IN/9ujfxGasnVA279FhZMVZ/AfTEN7vmzaWaAW4rcVFh6b/lNkGcQhPWHsH7qP8eze9Xlwx5Rp5pz0TuvrbWlc1VvukL6wPd3QtxG+GQC3PJNs35IFOHnTlx6Pmcy8hka7e/s5rQ4Xxz8AoDpHafXayX7imzj5g9nHCa7KBtvQy2zFEK0RGd2qEPt8s9Vvb7LFLj6w/IHshqNeo8U1l/N2qcdhu7XwpR3ah9kegXBmKfUGj6n1lee5vTgMvV/4qa31QKwU9+DdiNr+w6bt4xT8Os/1M9HPVm5VoEzRAwBU5jaw+L46laTtW10VqsaqFcM3JP3g7Wk8rZ+0ervZPgA6DC+fu63WmERvDrffVxzzTUsXryYwsJCjEZ5uuUUcZvgm9ugIBO8I+zdxExGNYiyZebddG4EugeSmp/KafNpPPQmNh5V/wGO6OjLzyd+5vO/PreP/dyUsInvr/oeX2MTz0q6+cKMpfDZ5eofjt+fgemfOLtVLdPJ9WoX9NwUcHFVq4rmpsCRFeoL1K6F18xXi9w0V50mwl2/w9c3Qvox+GQ8XP+5Ot6uGYryd2fjMTidkefsprQ4RzKOsP3sdrQaLbd0vaXBztPGvQ3tvNtxKvsUMSkxjIuU7qGilfvrB/jxAXUse3BPtUu7pkIWz90fIodWfRMf2BXuXQfJf6lBRF2zf6A+BO44ofLyntepwfzyJ9R7k4VT1AcHQx5Ug5aWHlxYSuCHu9Xu9RFD1OvTFGi10ONadQrn/d+rwXzm6fL6CKZQGPk4+LVzdkubp/QTsP4/ag2v8x9wgfp7GTagLHjvD6H9GqbocCssglfnYP6FF17g999/5+abb+bjjz8mICCgPtslLmbP12pG3lqi/mLctFh9Wgx4GdXLahszD+q4+dT8VObvnY+rEkixTzLeeg3/3PkuyflqFzUPvQceOg9SC1KZs2UO7419D01T/6fjGagG8J+Mg/3fqYUyZCxU/bFaYP0b6h9oFLWS7/ULwLetmolP3KU+fdW7q4WFvMOc3OB6ENwD7lkDi29Sn+x+cbX6D2jwfdBtWrMaS18+PV2Bk1vS8tiy8hOiJhDqGdqg5xoUPIhT2afYcXaHBPOi9VIU2DAX1r6ift15Mlz7ce3nLde7qV3vG1LXqdB2JPzxglow78BS9RXaV+1G3OPa6qfKa+7WvQaJMWDwVofbNaXK8T2uU4P5oyvh/0aV9yi02btEnTp31Gz7PbW4iJyz6pSOu78Aa1ncoTOqPR1tgXtYf/CJapwHWa2wCF6df8Mee+wxOnfuzLJly/jzzz/p37//BeeZ//TTTy+poaKM1Qpr/w0b56pfd5umZkMrjMkyuamZ+ZzC8i4tHXw6sDN5J5sSNwHg6gdWIDkf/I3+3NbtNm7ofAOJ5kRuXXEra8+s5buj33FD5xsa653VXVg/9Z/j9v/C8lnwwFZwbaLT6zUjGSfWcGj9S5jTj2IyGvDuOAnTmOcweQbh6eKKS+QQiBxSaT9FUSi0FGJ0MTb9h0HV8QpWp69b+bT64CwxBpbGqFMiDrhT/Xnz8Ce7KJtDGYcA6B/YH30TG1oQ6adOZRafLpn5+pSWn8aKU2qPlNu73d7g5xsUPIhvjnzDjuQdDX4uIZoccwocWQ5/LVWHQIFatG7CS6B1cW7bLsTNRx0rPuBO2DZfneM8KRaW3Q+r/wVXvKEG9S3JyfWwsawg81XvgU+kc9tzvpDe4N8B0o+rgbxGC5HD1B6FJ9fBiTXqVHp7vlJ7Uox4TC2W29qd2ggb3lTrU5hC1OEKXiFQkg8xn0NpWcKg40S16HFYP+cOtWxlRfA0iqIoddlRq9Xa55G/6Ek0GiwWS11O02hycnLw9vYmOzsbk6mJTJ9VlU3vqE96Qe0ONPa5St3EVh9M4Z4vYugd4cNPDw0H4FzBOZYeW0phaSFLdpzhXF4RE7sFMb5jd65odwUGl/InxF8c+II3Y97E4GLgmynfNFiV5npVZIZ5g9WxUCNmwfg5zm5Rs5OWn8ay48v4K2krB1P2kEwV45sq8NJ7YTKYMLma0Gl15BTnkFOUg7nYTKlSitHFSJhnGOFe4YR7hRPhFUFXv6508euCu752D1vMxWYSzAkk5CaQmp+Kv9FfPa5nON4G71o9NCgsLeRo5lGOZB7BU+9JN/9uRHhFoNVU090yNxV2LaB056ccKMlgp9HIQTd3Dnr5kViaW/79cPXisojLmNh2IkNDhtYosFcUhYzCDBJyE0gwJ5BTnEMHnw508euCl+uFbyDO5p5l29lt7EjeQYm1hIFBAxkcMpgoU5T9+3EwKYfJ72/E111P7PMTa/w9AsgvyWdDwgZKlVK6+XUjyhSFS1O+cW5EH8R+wP/2/Y8+bfrw5eQvG/x8mYWZjPpGrWS/7oZ1+LuV1z/YlLgJnVbH4ODBzffhmRDny4yDQ7+qXaDjtwFl95oaF5j8pjpTSnOTd668cF5OorpswF0w6dXmXyRPUdQAePkTamDXd4Zas6ApOrVRbWvUMLV3h0eFnsWnNsAfL6oP8AFM4TDl7eY9fPBSJO2BNS+pDzkuJGIwjJsDbYc3SrMuaucn6mwV0ePU4bjNVE1j0zoH8wsXLqzV9jNnzqzLaRpNswnmC7LU8VeDH4C+t1a5yfaT6dz4v220b+PBn4+PcViXnF3IkNfWoNHArucm4OdRucuwVbHy4JoH2Zy4mU6+nfj6yq8dgv0m6/ByWHKLOlbmvg0yXV0NKYrCz4cW85/d72K2OHbHbqt1w8+3PTmWInKK1UC9oPTSumxr0NDOux3d/LvhpnOzPwTIKc4hr8Qxg6ygkFWURXZRFeOvynjoPQj3DLcH9+Fe4YR6hmKxWtRjl7X7bN5ZDqYf5ETWCSyK48NFT70nXf270tm3M75GX0yu6kMKL1cv4s3xbDu7jZjkGHJLciudP1xvosBFR3phRvnxtK4EGP3UrmbVsCpW0grSqv1+RnpF0s2/G4HugWgoD9JyS3KJSYlxmJ2iomCPYAYFD8LX4EuxxcrCLXEAPDK2D9d0nHLRLuGHMw7z/dHvWX5yucP7ddO50dWvK90DunN9p+tp5906xxUWlhYy4fsJZBVl8faYt5kQVcWY2QYw/efpHM08ypuj3+TytpejKAof7f2I+XvnA9A3sC+P9XuMfkH9GqU9QlTp7D41EGrTRe1mW7GXXEEmHP1dHU+euEstfmXrghvWX30of+gXOPSzWjCrorD+alG77tOaxywpF2IpUbuib3wbUCCohzp8LaCjs1tWN0W5aq/Ifd+oX0dfBjcuUgsVN0eKoj5E+v1ZyCr7P9tjOlz+ujq0syUpLVarzCfFQvF59zdJseo0jKDeV/e/U52y0ZyiPowyn1V/Z7tNg85XNK1aEAm74JPL1HH6s080rbbVQoMH8y1NswnmASylFxyDdOhsDle8txEvg44dz47HzbU8m/bNznie+mE/fSJ8WPZQ9U/QzhWcY/rP08kozGBq+6mMiiif31iDhl4BvQjxDKmf91Ofltyq/hEOH6QWMruU4jYtgdUKx/9Qp4w7swM8/MErVC304hVMSnY8L2bHslGv/hnoWlTMlbl5dAvuT9cxL+AZWnnqyRJLiT1ItgXipdZSvFzLM/Week8yCzM5k3vGnlE/lXWKgxkHSc1PrdNb8TP6Ee4VTqBboJrNNieQWlD3Y3Xx64K52MyRjCMUW4trtJ/J1cSg4IH0smjpemQNXdNP421VsAC7jQZWebjzh7s753Q1z2Br0BDkEUSYZxieek+OZR4jKS/povu5aFzoHtCdISFDMLgY2H52O7GpsZRUVTG2wrmGhw3nuo7XMSpiFHqtntT8VA6mH+Rg+kE2Jmzkr/S/7NuHe4bj7+bPkYwjFFoKHc49rcM07u99P8EewfbltgcBf5z+gyJLkcO5/Yx+TImewjUdrnHYpyk4m3uW1adXk1WUxfWdrr/g37bvjn7HS1tfIswzjOXXLG+03gr/2fEfFh1axPWdrue5Ic/x+o7XWXx4MQB6rd5+3UeGjeTRfo/S2a/zhQ4nnCk7US0cagpTg9S6js0tyFSrpVtK1C6vphD177vOUF7TJHG3elNu8IQuV0KXqWoXWI1GDVqSYtUA+sgKtWhVaF91fVh/CO6lBjOJu8vroxTnqnVpul4F7Uar41MtJWoAvv1/cGZbefs0LhDYDcL6QtYZtYu8tbT691ORRgtRw9XzdLmyZdRjOd/xNeoMMfnnQO+hzqmdn1E2p30SFGapgfGg+9QhbU0xIEner84OkH5cvWZjn1V7R7aEe6/iPFj7Kmz7SC36a/RR31+Pax2z+U2d1Qp5aWBOUse35ySqxeoSY9SHb+f9r66k5/VqTaTm9BCtpBBeC1P/3jz2V7MdNy/BfC01q2D+IopLrYx5cy1J2YU8PLYDT0wqv6l7YNEufvsrmcfGd+Sx8Z0ueJwNCRt4aM1DVa7ToGFE2Aiu63Qdo8JHXXBappPZJ1kVt4qjmUfpF9iP8VHja3QzX2It4YsDX3Ai6wR9AvswJGQIEV4RF+5Kmp2gdrcvzoVeNzn+Art6qMtMTfAhxKUqPe+PcXGeWshl58eQcbLS5hZgmacHb/n5YnbRolcUHixy4Y6AQegG36dW3G0g5wrOcSj9EIczDlNqLbU/ADC5mvDQe1Tq7u7p6km4Z3iVXfMLSwtJyk0iITeBM+byBwdnc8/i6uKqPmAoO7a/mz9d/LrQzb8bQe5B9p+jEmsJJ7NOcjD9ICezT5JdlG3P5ucU5+Bj8GFIyBCGhAyhi1+X8uDNUqLOZbruP+pcwy4GMIVg8QrhqE5LwdlYtYAgqJX+O09SMzABne0Fm/yMfoR6huLq4thDJrMwk0PphziYfoCc4hyokJnXaXX0DOjJgOABeJUUqYV8ALpdTYGLjtiUWGLTYikq+5n4MTaBVHMR3drmcNy8x34cf6M/Go2GcwWOUzvptDrGRY5jesfpDA4ZjFajxWK1cCpbfRizOm416xLWAeCqdeXmLjcT5R3FD0d/4ED6gYtef61Gy8iwkUzvOJ2R4SMbbEq3i0nMTeSP03+wKm4V+87tsy83uhi5p9c9zOw+s1KPJKtiZdpP0ziVfYonBz7JjG4zGq29a+PX8sjaR4jwiqBnQE9WnFqBBg3/HPxPxkaMZf6++fx47Ed7r5OH+jzEfb3uk6735mQ48lt5t+YLcfdXu9Oef9NqKVED3h0fw7kj6nzlXaeq40OrGk9rtVYdzORnqEPldvwPSssfjuEdoQbQ/h0cq7KjUdsSPkDNYtuOmXIQdvwf7PtWHbNaW6Ywtdp7/DbISaj9/jaunupMH4m71AAU1AxexBA1uMtNrrxPYDf1e9dulDp9WeIuNahIOajuGz1WXd/pCvXhc0tnTlYrv9tqAVQnuGd5UH92b/m0X8n71S76JttD+hD1upjPlj8UyE2FwC4w8B41w1xxfnVb1//YRYCidjvvOlXtNq11UX+WE3aqD2sO/6oOf6iKVyhc92nTmIKuviXFqgWnk8v+T9jG2XedqmaqPc97GKfVN42HGYU5sPVD2PZfKMqpfjujj/r35/z34eoB/WZCSK8GbWaDmT9C/f24cVGzHTffaMH82bNn+emnnzhy5Ag5OTlVjqFvDgXwWlIwD7Dyr7Pcv2g3ri5aVj42kvZtPCmxWOn30mrMRaUse2g4fSJ8Lnqcrw59xZp4x7Ey+SX5DjftbdzacHm7ywlyD7IHTu56d/ak7WFV3Cr7lHcV9WnTh4ltJzIxaiJBHpWzEmfMZ3hqw1PsP+fY1S7UI5QhoUMYFT6K4aHDMVbVjXnbfFj5lP1LC5Dm4kK6iwvuWldMfWdgGvE4eo82lFhLSM5LtgeB6QXpdPHrwoCgAXi61qxCbmZhJukF6UR5R6HX1q3gR2JuIkuPLWVv6l6Ghg7lhs43XHTMNMV5are2HR9D6sHqtzN4q/Pr9rwOpTiPdQnreT9hFceLMwHo5RnFS8NfIjpYuufWiaVU/Ufp5uuYOTEnq4VhYj6DvPN6EPh3VDNfnm0cl5cWl9+Amc+qxzB4lc/DaqsIe3KdGlzEb1EzBgCuXtDnFhh0LwR0KDteEW99uZTs41uZ3Nmb0OGj+SFzP8tOLCOjbFiAVqOlvakt3XQmemjcmdj3XvxD+1/wLe9J3cO7u99lV8ouh+U6rY7xkeO5puM1hHuG25crKPx17i++P/o9MSkx9uW+Bl8ui1TrDAwMHljn35+aSjAnsPr0albFrXLogaBBQ7+gflitFmLT9gAQ4RXB04OeZkjIEPsDo90pu/l4/8d46j1Zfd3qGv+NqA85xTmMXDISa9n11ml0/HvEv5ncfrJ9m9M5p/kw9kNWxqkPeG7teitPDnyy+noQDSHlIBxbpd4Ath1ZuQiS1aIGkfFb1MAu+rK6jxfOO6fOYlKUW5aVDlEDGo0LHCvrzn1mB/bx1jUV1EO9+Yu+TP1di/msPFityMVVDWY92qi/s7bf2+JctZpyWD/19zakj9pDavP7YBsyFNIHLMWQeqhm7TN6q1M5WUrg9Kby5YHdwb992fnPqgG0YlX/Ttj+boT2U7Nyh36Bo6ug4nAmvQd0HK9mwE1hauCSWBYoZsapf9fs00kNUAPuIyvU8ezmCj2IPALV8d8D7lQLiILaAyExRj2m0UftJm/723S+krLhRs197HhdWC2we6GaLbX9DJtCAY36wHj/d44Pfy6Fmx/0n6n2qtj3rTrNX1WZWY826oODMzvUaWgvpNMVcPW8lv3wxVKqjsPe81V5UF8dnZtaaM8+BVtf9aGILTNuTgI00OfWyvcAFVktdSvwWFIIMZ+qMz8U2Ib/adRg3Va8zjtCbVf4APWBYUt86PvTw+rvz8gnYNy/nN2aOmmUYP6DDz5g9uzZlJSUd+u0Hc6WDVAURQrgOYGiKNy5YCfrjqQxsmMAX9w1iO2nMrjpf9vw83Al5tnxaLV1/+WNz4nnh2M/sOx4eVBQHZ1Wx9CQofRs05OtSVuJTY21r7Nl6a7rdB0jwkag0+pYcXIFL217ibySPLxcvbi2w7X8lf4Xe9P2Ulqhi567zp3R4aOZ2HYifQP7klaQpgblOfEkHF9JQkEaCdZ8kiwFlFRxs+Sm0VGMUmn8NKjdiHsE9GBIyBB6BvTE2+CtPqgwmDC4GNiftp9tZ7ex7ew2DmccRkFBr9XTybcT3fy72V8dfTpWWwitxFrC+jPr+f7o92xJ2oJSoY0eeg9uiJzEbYongYW5jv/gdQbYs1h9kl7FWPISYI/RQIlPFN7dr8HUbTomz2COZh7lvd3vsTdNnYrFy9WL+3rdx21db5PCZg2ptFjtCnv0t/Ib5PoU0kd9sJN+rHxZ25HqzXHyPjVgqMi3LSVdrmRnm3a452fQKX437ifWOt7QdZigTsUXPc4xw2C1qHU7FCuKorA5ZSf/O7yI/JJcpgb0Y6p7FH4F2WqQ5R+t3swEdisP6HJTObX3S344voyfLRlkupQf28fgw/Cw4bRxa2N/KGgymOgb2Lf6njwFmep8waDO7ezmW2kTRVFYduxHlhz5hoMZ5Q+9tBot/YP6MyFqAuM92tPmjxdREmJY0Xkkb1lSSSvOAtRAXznv78ft3W5n9sALzJ1clKtmNS71Bqk4D4rLM683/fkABzKPYNS68lb0TYzSeqnBo9Gkfq9D+4LRm68OfcXrO14HYGr7qbw4/EXHByWKon7vrDX4v2z0rtl0jJlxsPa1snGzZd8vo486lrLLFDXwPfwLHF6hdiu20btDh7JgMqyfmkW0jcfMTSnvih7cqzyjmLhbfYhZXSByvrAB6vfmgg81FEg7AnGboIr/CWqweqeaUT6+Rs1Upld+UH1RQT3UQlEdJ6g/H0VmtchUYox6o1+RtQRSDqiZ2IrBnMZF7Xo++D61K3rFnzOrRc3WV1eBu6RQfUCRGKMG+dFjqw+gi/PU61PVz7HVCkm74eRa8GkL3a5uVtN2Niv5Geq0XzGfqg94g3uV1xkI7aM+4Kn4ELg4T32gYrtvcPNTs+o7P626J4ZtujyDl7qdbciFjcEEnS6HrlPUjL2mwv2C1qVh5gpvyjLj1BpNh35xLM5YW3oPGPawOjODsSzusJSo12D7/yB+qzrnfcWHaUaf8r+POUlq9/mKf8cVa1lPpLLr7N9RDWQ7T3ZudXlnaAFF8Bo8mF+zZg0TJkzAZDLx0EMPsX79erZu3cr8+fM5evQoS5cuJS4ujkcffZTevXtLATwnOJ2ex4R3NlBcamXeLf3Yn5jN/PUnuKZvGO/c2KdezlFiKWHtmbXEpMSQXZRt75psLjYT4RXBhKgJjIkYg7fB275PSl4Kf8Sr3Vt3p+62Lw90D6Szb2c2JqrdzfoF9uP1ka/bx6/ml+SzO3U3mxM380f8HyTnVdGFrxo6jQ4/Nz8KinIwWxyfcBsUCNN5EO4RirdXGHtzThJvjq/V98FN51ZlITOdVkdHn472iump+an2yuWJuYkO44qHtOnLUJ/O/Bz3GydK1H+kOkXhytw8bjDn0rOomEq3VL7tYNC9FHe7iq3p+1l1Zi1rEzdhLjFX21aji5Hbut3GHd3vcLguopHknSsfx1p83nXS6ivchIWpn+ello1X3aV+zDih3lB1maLeXPlEqsHZybXqDcDRlVS8uSjSe7O1sC0mDyP9SvdWn+Hxa68+rT+1oXx/v/Zq8GE+W5ZROFt1oHMhtiyFRuNw41MCxJTVGVhj8iYTa5W7GzUu/D1qCrcOeBQXj7IsRsoB2F7Wzdj2e6dzg943qj0TgrpDSSHZR5fzwp4P+aNUDR61wEC/HkzoOI1xUeMI0Jtg87vqlDsVHnrkajT8X0RnFrkUUooVN42OcMWF8MI82hfm8TfFhFdo//IbLGtp+ZjixN1q5sUjsKw3RT/1ZswruPwGLKcsUDWazsvEUT7OOSEGzh11uJZ/uLvxhbcXj2Vk06+oqiBWAwGdIKQ3vyg5/CvvEBYUxugDeFMfhdFeuCj5okFwtlbDVjc3dIpCuN6LMLcgvEzh5eOybZlwd391+saYz9TgE9SHSWmHIS+NEmCnmxGdojCgsAgtqDekbUeoQWr2mQu2w06rU38WNVo1iLQJ7QdB3cp/PnOS1AC5bdl4686TazfeOj9DvRk+/Kta9bpNZzVo7na147zgSlnwf3Slev3t3ZxD1eA4eV95V+ikWLVw1uin1Hmua9sF11IW1CfuUgO1HteCd/jF9xMtT3VDOGrCUqo+VN7+f+pY6U4T1SA+fIDjAxtLidrtP2GXWu+g7Sh5UFOdkgL1+1VRbkqF/wdlQyF0Bse6FueOqH8XQH3YMnJWWUb9M8ceL3VlCoMxT0PvWy5YY6tFawFF8Bo8mL/qqqtYvnw5W7ZsYfDgwdx555188cUX9gx8UVERDzzwAEuXLmX37t20b1/7wgnz5s3jzTffJDk5md69e/PBBx8waNCgarf/7rvv+Ne//kVcXBwdO3bkP//5D5MnT652+4paYjAP8M7qo7y35hjBJiPuBhdOpuXx3k19uLpP0ygmcyr7FEuPLeWn4z+RWaR2+9ZqtNzX6z7u7XVvteNprYqV/ef2szpuNatOr+Js3lm1QJpnOGFeYYR7qlOh2aZGC3IPsmefLaXF5O79mpwtb2PISiDAYsHhX2NoP5L63Mh2bz+2psQQlx1nL/aWW5yLgkKQexBDQ4cyJGQIg0MG42/0J8GcwIGMA/ZiYofSD5WNd66ef6mFa3JzudacR0Sp2uvACmx0M/KZjze7jeU3jx0VHdflFzMlM4PiyEEc7HQZBw2uHMw4RExyjEMA72/0x8/Nz14lvqC0AJ1Wx/SO07mv1320cb9A1y7RtCnKhf8pZZxSswZewRDWj+2ZJm78eDuRfu5seHSg2t338K9w4k/15qLrVPUV2E09bvoJNYNTTc+PKuk9KnRzDlMz5GmH1aD0/GOE9isbMztazeLGfEaptYRdRgN7A6PJLspUZ0/QaknQ6ThqUG8iexcW8XKJB+0MfuXTBoEa4IFakdcmpDe7zKd52tedZJ0OnaLwYGY215pz8bda1WCzx7Xqw4+0Q+o+HSaoDwL2fwsHloG1hGythlI0+FmtlR+kOYtWB57Bjt3Kc8se+NgqL5dZ5+bGE4H+FGm1tCsuoWdREeGlpYSXlBJeWoqfxYrJasXLakUHZGu1/Onuxu8e7mx3M1J63s+Zt8XisL/t8zYWCyarFVPkSAzjX6A4uAdbEzez6uDXrE2Nwayof9uiXTy4I3oaV/Z/BL2ru/qzfHZvWQXzXyArXi0GZ5vD2DOwbFx1jJqBsn8P9ND9GjXIrqq+x8V+RxpbU2uPEKLxVPUARlHU3j1rXnbsVQfqEIf+d0DPG9S/iYm7yl+lhY4PgD2DKmfcTWHQ+6bWOWSlohZQBK/Bg/ng4GAiIiLYuXMnQKVgHtSAPioqissvv5wFCxbU6vjffPMNt99+O/Pnz2fw4MG8++67fPfddxw5coTAwMpTQ2zZsoVRo0bx2muvMWXKFL7++mv+85//sHv3bnr06HHR87XUYL6wxMLEdzYQn6F21bzQlHTOVGwp5s8zf7I5cTNXR1/NgOCaF2BTFIUiS1HV4+cvvCNknirPeNoyKLbsknuA+gc1+jI1O5pzFmtOIgW5Kbj7tkdjG8NsGydWMXuSFItSlEuiUsxBax4HrfmcLc4mOCeF8NIS+81wqOKCS8UwwUWvZqzKCgDtzU/g2yPf8nvc7/YsvovGpcqhAW3c2jA+ajwTo9RhBxW7zpdYSlBQKhVaEy2fbTpKF62Gwy9fjt6lhlmdolz1ZqPIXH7z4BVSdvNQwyf9VqvakyAhRr0J6Tihckaxqu7ZZVlYJbQv3+ed5K3CU+RpwNWqcE92NqGlVnJCe5ET2occN/XvtakgG1PSXkxJ+4jTu/CptwmrRkOkiztv9Po73b2i1MJjh5c79i5wD4Ar/qMWhrIFXOYUtSjU3q/VLqW23/Ww/mpvmNQDjllXjUt5BfCw/uq0XOnHK9yExajDE+w3YWXfx8KcCtn6JPVvj22sZVhZVr82UyHlpqlZ65QD9q6XMYUp/D3lT3KV6mc5AHXYUpGlyOFvSwefaNy1riSYE8koufCDSRuDiwENGofZD/yN/hRZiuxTHQa6B3J7t9sZEjJEncXhYrUHFEXN4CfEqGNAu0ytewV4IYRoKiyl6v+Zbf9VhzkM+Js6/WLFXkCi7pp5EbwGD+YNBgPXXHMNS5YsAeDee+/l008/JTc3Fze38qdB119/Pdu2bePMmRp2pSszePBgBg4cyIcffgiA1WolIiKCv//97zz99NOVtr/xxhvJy8vj119/tS8bMmQIffr0Yf78+ZW2LyoqoqhCN8WcnBwiIiJaXDAPsPZwKncuUB+6XGxKulbPVtk15rOaVT8G8G2rBgQpf9WsSE1o37IqqFOhzYVnFLDJLspm+cnlfH/se45lHnOYq72bfzd6BvSkV5tejVvoSjQLVqtCl+dXUlxqZVSnNhh0TfNnJLT4FO0LD5Lk2pYzrh0o0ZbfzBQp6ZxWPiZDc6RWxwxgGO24DRdN+f8k39JUxuT8zKDctRw29uF7/3vJc2nZw02KlSxyOEwRaRSSRhFpFHGOUnKx4Pg3y50I/BiAPwNx05TP/GFRCijkXNm+aRSSShHnKCSVEsxYyKfikAA9PvjRH38G4kVHLBSSyjrOspoSshzOqcMTA21wxRuaTh8IIYQQzVhE0XH8S1Mo1LpTpHFM+N098F+M7HeVk1pWMzUN5us8kMLPz4+8vPKKqL6+auGh+Ph4OncunwrNYrGQnp5eq2MXFxeza9cunnnmGfsyrVbL+PHj2bp1a5X7bN26lVmzZjksmzRpEsuWLaty+9dee40XX3yxVu1qrsZ2CWRS9yB+P5DCpO5Na37nJscjAEY9AcMfU7six3yqZg7tY0RD1Uz8uWNqxu3cUXW9raiZ0bs8O+dxXld2vRu0H1unrj7eBm9u6XoLN3e5maS8JHwNvlVO1SbE+bRaDV2DvdibkM2Go2kX38Fp3AFbj5ysKtbfgc57F3rvXaDoUCxuKFY3FIsaqGtcCtBoC9C4FIDGQknWQE7l9OUUOYBjVvlbpgJTIRc4VwjU4CFcs9eu7HU+CxqXQtAWgKLDXOpDee3q86tYuwJhZa/zWUFbpF4HTSnW4gAy0KKWiLP93PUHTW/0plj0PjFoXNPQ6vIpJZdScsmr4qhCCCFEXWQaAIPtXtnx//y57HqoTdBE1DmYj4yMdMi29+jRA0VR+PXXX+3BfG5uLhs3biQ8vHaFWs6dO4fFYiEoyLEbXVBQEIcPH65yn+Tk5Cq3T06uukjaM8884xD82zLzLdV7N/Vlw9E0xnapRZfN1sxFp3Z16j7twtsVZKndbPPT1ari/tENOjZSo9EQ5tk06h2I5mPerf3YeOwclzYRaVPQC7jT2Y0Ql6wvcBcARZZ8skqSySpJJt9SwxoNQgghxEVorFZ8zYdxsVQuUN29/RAntKhh1DmYHz16NO+88w4pKSkEBQVx5ZVX4uHhwT//+U+Sk5OJjIxk4cKFZGRkcNNNN9Vnm+uFwWDAYGg9Y1KMehcmSla+/rn5qFP7CNGEhfu6c/OgSGc3Q4hqdHF2A4QQQohmqc7B/PXXX09sbCx79uxh0qRJ+Pn58fbbb3P//ffz9ttvA2phsrZt29a6O3tAQAAuLi6kpDh28UtJSSE4uOqANDg4uFbbCyGEEEIIIYQQzVWdKyENHDiQ1atXM2nSJPuye+65h507d/Lkk09y99138/bbbxMbeXcOTwABAABJREFUG4ufn1+tju3q6kr//v1Zs2aNfZnVamXNmjUMHTq0yn2GDh3qsD3A6tWrq91eCCGEEEIIIYRoruqcma9Ov3796Nev3yUfZ9asWcycOZMBAwYwaNAg3n33XfLy8rjzTnW85O23305YWBivvfYaAI8++iijR4/mrbfe4sorr2TJkiXExMTwv//9r0bnsxX1z8mp2fQ7QgghhBBCCCFEfbPFpBebeK7eg/n6cuONN5KWlsbzzz9PcnIyffr0YeXKlfYid/Hx8Wi15R0Lhg0bxtdff81zzz3HP//5Tzp27MiyZctqNMc8gNlsBmjRRfCEEEIIIYQQQjQPZrMZb+/qp9Ct8zzzLY3VaiUpKQkvLy80DViNvD7YKu+fOXPmgvMOiqZNrmPLINexZZDr2DLIdWw55Fq2DHIdWwa5jo1PURTMZjOhoaEOCezzNdnMfGPTarW1nkLP2Uwmk/xCtQByHVsGuY4tg1zHlkGuY8sh17JlkOvYMsh1bFwXysjb1LkAnhBCCCGEEEIIIZxDgnkhhBBCCCGEEKKZkWC+GTIYDMyZMweDweDspohLINexZZDr2DLIdWwZ5Dq2HHItWwa5ji2DXMemSwrgCSGEEEIIIYQQzYxk5oUQQgghhBBCiGZGgnkhhBBCCCGEEKKZkWBeCCGEEEIIIYRoZiSYF0IIIYQQQgghmhkJ5puoefPm0bZtW4xGI4MHD2bHjh0X3P67776jS5cuGI1GevbsyYoVKxqppeJCanMdP/74Y0aOHImvry++vr6MHz/+otddNI7a/j7aLFmyBI1Gw7Rp0xq2gaJGansds7KyeOihhwgJCcFgMNCpUyf529oE1PY6vvvuu3Tu3Bk3NzciIiL4xz/+QWFhYSO1VlRlw4YNTJ06ldDQUDQaDcuWLbvoPuvWraNfv34YDAY6dOjAggULGryd4sJqex2XLl3KhAkTaNOmDSaTiaFDh/L77783TmPFBdXld9Jm8+bN6HQ6+vTp02DtE9WTYL4J+uabb5g1axZz5sxh9+7d9O7dm0mTJpGamlrl9lu2bOHmm2/mb3/7G7GxsUybNo1p06bx119/NXLLRUW1vY7r1q3j5ptvZu3atWzdupWIiAgmTpxIYmJiI7dcVFTb62gTFxfHE088wciRIxuppeJCansdi4uLmTBhAnFxcXz//fccOXKEjz/+mLCwsEZuuaiottfx66+/5umnn2bOnDkcOnSITz/9lG+++YZ//vOfjdxyUVFeXh69e/dm3rx5Ndr+1KlTXHnllYwdO5Y9e/bw2GOPcffdd0sg6GS1vY4bNmxgwoQJrFixgl27djF27FimTp1KbGxsA7dUXExtr6VNVlYWt99+O+PGjWuglomLUkSTM2jQIOWhhx6yf22xWJTQ0FDltddeq3L7G264Qbnyyisdlg0ePFi57777GrSd4sJqex3PV1paqnh5eSkLFy5sqCaKGqjLdSwtLVWGDRumfPLJJ8rMmTOVq6++uhFaKi6kttfxv//9r9K+fXuluLi4sZooaqC21/Ghhx5SLrvsModls2bNUoYPH96g7RQ1Byg//vjjBbd58sknle7duzssu/HGG5VJkyY1YMtEbdTkOlalW7duyosvvlj/DRJ1VptreeONNyrPPfecMmfOHKV3794N2i5RNcnMNzHFxcXs2rWL8ePH25dptVrGjx/P1q1bq9xn69atDtsDTJo0qdrtRcOry3U8X35+PiUlJfj5+TVUM8VF1PU6vvTSSwQGBvK3v/2tMZopLqIu1/Hnn39m6NChPPTQQwQFBdGjRw9effVVLBZLYzVbnKcu13HYsGHs2rXL3hX/5MmTrFixgsmTJzdKm0X9kPuclslqtWI2m+U+p5n6/PPPOXnyJHPmzHF2U1o1nbMbIBydO3cOi8VCUFCQw/KgoCAOHz5c5T7JyclVbp+cnNxg7RQXVpfreL6nnnqK0NDQSjcwovHU5Tpu2rSJTz/9lD179jRCC0VN1OU6njx5kj///JNbb72VFStWcPz4cR588EFKSkrkxsVJ6nIdb7nlFs6dO8eIESNQFIXS0lLuv/9+6WbfzFR3n5OTk0NBQQFubm5Oapm4FHPnziU3N5cbbrjB2U0RtXTs2DGefvppNm7ciE4n4aQzSWZeiCbo9ddfZ8mSJfz4448YjUZnN0fUkNlsZsaMGXz88ccEBAQ4uzniElitVgIDA/nf//5H//79ufHGG3n22WeZP3++s5smamHdunW8+uqrfPTRR+zevZulS5eyfPlyXn75ZWc3TYhW7euvv+bFF1/k22+/JTAw0NnNEbVgsVi45ZZbePHFF+nUqZOzm9PqyaOUJiYgIAAXFxdSUlIclqekpBAcHFzlPsHBwbXaXjS8ulxHm7lz5/L666/zxx9/0KtXr4ZspriI2l7HEydOEBcXx9SpU+3LrFYrADqdjiNHjhAdHd2wjRaV1OX3MSQkBL1ej4uLi31Z165dSU5Opri4GFdX1wZts6isLtfxX//6FzNmzODuu+8GoGfPnuTl5XHvvffy7LPPotVKTqM5qO4+x2QySVa+GVqyZAl333033333nfQ+bIbMZjMxMTHExsby8MMPA+q9jqIo6HQ6Vq1axWWXXebkVrYe8l+siXF1daV///6sWbPGvsxqtbJmzRqGDh1a5T5Dhw512B5g9erV1W4vGl5driPAG2+8wcsvv8zKlSsZMGBAYzRVXEBtr2OXLl3Yv38/e/bssb+uuuoqewXmiIiIxmy+KFOX38fhw4dz/Phx+8MYgKNHjxISEiKBvJPU5Trm5+dXCthtD2gURWm4xop6Jfc5LcfixYu58847Wbx4MVdeeaWzmyPqwGQyVbrXuf/+++ncuTN79uxh8ODBzm5i6+LkAnyiCkuWLFEMBoOyYMEC5eDBg8q9996r+Pj4KMnJyYqiKMqMGTOUp59+2r795s2bFZ1Op8ydO1c5dOiQMmfOHEWv1yv79+931lsQSu2v4+uvv664uroq33//vXL27Fn7y2w2O+stCKX21/F8Us2+aajtdYyPj1e8vLyUhx9+WDly5Ijy66+/KoGBgcorr7zirLcglNpfxzlz5iheXl7K4sWLlZMnTyqrVq1SoqOjlRtuuMFZb0EoimI2m5XY2FglNjZWAZS3335biY2NVU6fPq0oiqI8/fTTyowZM+zbnzx5UnF3d1dmz56tHDp0SJk3b57i4uKirFy50llvQSi1v45fffWVotPplHnz5jnc52RlZTnrLYgytb2W55Nq9s4jwXwT9cEHHyiRkZGKq6urMmjQIGXbtm32daNHj1ZmzpzpsP23336rdOrUSXF1dVW6d++uLF++vJFbLKpSm+sYFRWlAJVec+bMafyGCwe1/X2sSIL5pqO213HLli3K4MGDFYPBoLRv317597//rZSWljZyq8X5anMdS0pKlBdeeEGJjo5WjEajEhERoTz44INKZmZm4zdc2K1du7bK/3e2azdz5kxl9OjRlfbp06eP4urqqrRv3175/PPPG73dwlFtr+Po0aMvuL1wnrr8TlYkwbzzaBRF+pkJIYQQQgghhBDNiYyZF0IIIYQQQgghmhkJ5oUQQgghhBBCiGZGgnkhhBBCCCGEEKKZkWBeCCGEEEIIIYRoZiSYF0IIIYQQQgghmhkJ5oUQQgghhBBCiGZGgnkhhBBCCCGEEKKZkWBeCCGEEEIIIYRoZiSYF0IIISqIi4tDo9HQtm1bZzfFadq2bYtGo7G/xo8f7+wmNUtjxoxBo9Gwbt26SzqO0Wh0uB533HFHvbRPCCFE8ybBvBBCiFbFFqjGxcU5uykN5oUXXkCj0fDCCy9c0nGmT5/OzJkzmTRpUv00TNTJjBkzmDlzJsOHD3d2U4QQQjQhOmc3QAghhGhKwsLCOHToEHq93tlNcbq5c+e26h4KTcXHH38MwIIFC9i8ebOTWyOEEKKpkGBeCCGEqECv19OlSxdnN0MIIYQQ4oKkm70QQohWYcGCBWg0Gk6fPg1Au3btHMYh28Y1X2jMvG1bgEWLFjFo0CA8PT1p06YNN998M/Hx8QAoisKHH35Inz598PDwICAggDvuuIPU1NRq23f06FHuu+8+oqOjMRqNeHt7M2rUKBYtWlSr96nRaHjxxRcBePHFFxtsrPWuXbu48cYbCQ8Px9XVFZPJRPv27Zk+fTo//fRTtfvceuutREZGYjAY8PPzY9KkSaxYsaLa85SWlvLZZ58xfvx4AgICMBgMhIeHM378eD744IMqt58/fz7Dhg3D29sbo9FIx44deeSRR0hMTKzyHBWv6w8//MCIESMwmUx4eHgwfPjwC7bvzJkz3HXXXYSEhNjP9eyzz1JQUFDtPtnZ2Tz33HP07NkTDw8PDAYDoaGhDB8+nOeff56SkpJq9xVCCCFsJDMvhBCiVejQoQMzZ87k+++/Jy8vj+nTp+Pp6WlfHxwcXONjPfPMM8ydO5dRo0ZxxRVXsGPHDpYsWcLmzZvZu3cv999/Pz///DNjxoyhffv2bN68mYULFxIbG8vOnTtxdXV1ON53333H7bffTmFhIV26dGHy5MlkZ2ezfft2ZsyYwZ9//slnn31Wo7bNnDmTPXv2sHfvXnr37k2fPn3s60aMGFHj93gha9as4YorrqCkpITevXszdOhQLBYLiYmJLF++HIvFwtVXX+2wz3vvvcesWbOwWq306dOHwYMHk5yczLp161i1ahUvvvgizz//vMM+2dnZTJkyhU2bNqHX6xk2bBihoaEkJyezb98+1qxZw9///nf79kVFRUyZMoU//vgDo9HI2LFjMZlMbNmyhQ8++IDFixfz+++/069fvyrf15w5c3j55ZcZNmwYkydP5vDhw2zZsoUpU6bwww8/cM011zhsf/jwYUaPHk1qaiohISFcddVV5OXl8c4777B27doqz5Gfn8+IESP466+/aNOmDePGjcPDw4Pk5GT7+WbNmoWPj08drowQQohWRRFCCCFakaioKAVQTp06VeX6U6dOKYASFRVVaR2gAIq/v7+yZ88e+/L8/HxlxIgRCqD07NlTiY6OVuLi4uzr09LSlA4dOiiAsmjRIodj7tu3TzEYDIrRaFR++OEHh3VxcXFKz549FUBZuHBhjd/jnDlzFECZM2dOjfep6GLfo7Fjx1b5XhRFUbKyspStW7c6LFu5cqWi0WiUgIAAZf369Q7r9u3bp4SHhyuAsm7dOod11157rQIoffv2rdSWkpISZdmyZQ7LnnrqKQVQoqOjHbYvLi5W/va3vymA0q5dO6WoqMhhP9t19fHxUbZt2+awzva97NSpU6X3OnDgQAVQbrjhBqWgoMC+/PTp00p0dLT9uGvXrrWvW7hwoQIoV1xxhVJcXOxwPIvFoqxbt65S+2w+//xzBVBmzpxZ5XohhBCti3SzF0IIIWrppZdeonfv3vav3dzcmDVrFgD79+/n/fffJyoqyr4+ICCABx54AFCz2hX9+9//pqioiFdeeYVrr73WYV1UVBSffvopAO+//36DvJe6SElJAWDy5MmV1nl7ezNkyBCHZXPmzEFRFObPn8+oUaMc1vXs2ZO3334bwKHb/N69e1m6dClGo5Fffvml0rAHnU7nkP0vLCxk3rx5ALzzzjsO2+v1et5//32CgoI4deoU33//fZXv66WXXmLw4MEOy5555hm8vb05evQoZ86csS/fvHkzO3fuxMPDg48++gij0WhfFxkZydy5c6s8h+17N2HChEpFFrVaLaNHj67Uc0MIIYSoigTzQgghRC1VFcR27NgRUIPMiRMnVrs+KSnJvsxqtfLbb78BcOONN1Z5rgEDBuDp6UlsbCyFhYWX3Pb6MGjQIABuvfVWNm3aRGlpabXbnjt3jh07duDm5sbUqVOr3GbMmDEAbNmyxb5s5cqVAFx55ZWEhYVdtE0xMTHk5ubi5+dX5Xnc3d256aabAKrtAl/VfgaDgfbt2wM4jLm31Vi4/PLL8ff3r7Tf1Vdfjbe3d6XlAwcOBOCNN97giy++ICMj4yLvTAghhKiaBPNCCCFELUVGRlZaZht/HxISgk5XuSSNl5cXgENAnp6eTk5ODgAREREOxepsL61WS25uLlarlfT09IZ4O7X22muv0a9fP3777TdGjhyJyWRixIgRPPfccxw6dMhh21OnTqEoCgUFBRgMhirfY2BgIABpaWn2/WyFCms6s4At0G7Xrl2120RHRztse76qriuAyWQCHK9dQkLCBc9XXRHFMWPG8NRTT5GamsrMmTMJCAigc+fO3HXXXfz0009YrdZq2y/E/7N33/FRVWkDx393Sia9904gdAgd6V0QQVHsS3UtqNhAVNQFUV7dFeyirKCAoGLHBkjvvfdOSEhI75mUycx9/8hmNCaB9Eng+e4nHzf3nnvOc3MJzHNPE0KIv5IF8IQQQogq0mgqfhd+tXN/99fEbfz48dcsbzAYKl13XfL392ffvn1s3ryZdevWsX37dnbv3s327dt54403ePPNN3nhhReAP+/R2dmZ0aNH2zLsa6rKs6uJf//730yaNIlff/2Vbdu2sX37dhYtWsSiRYvo2rUrGzduxMnJqV5iEUII0XhJMi+EEELYiLe3Nw4ODuTl5TF37ly8vb1tHVKlKYpC//79rUPk8/PzWbx4MU888QQvvfQSd911F02bNiUkJMRa/vPPP690wlzSS37q1KlKlS8Zin/x4sUKy1y4cKFU2ZooqSM6OrrCMiWjC8oTHh7Ok08+aV2Nf+/evYwZM4a9e/fy1ltvWbcXFEIIISoiw+yFEELcUEoWF7vaPO/6otVqGTJkCADffvttrdVri3u0t7dn0qRJtG/fHovFwpEjRwAIDAykffv2ZGdnW+fBV8awYcMAWLlyZal1BipSsrZAWloav/zyS5nzeXl5LF++HIABAwZUOo6K9OvXDyie21/evPdffvmFjIyMStfXtWtXHn/8cQAOHTpU4/iEEEJc/ySZF0IIcUMJDg4G4Pjx4zaOpNjMmTOxs7Nj2rRpLFmypNw508eOHePHH3+sdJ11fY9z584lJiamzPFTp05x9uxZgFKr+c+ePRuAiRMn8uuvv5a5TlVVdu/ezZo1a6zHOnTowO23305eXh633357mfaKiopKJe329vY88cQTAEydOrVUr7jJZOLpp58mISGBJk2acNddd1Xntkvp06cPnTp1IicnhyeeeIKCggLrudjYWJ577rlyr/vpp5/YsmVLmedsMpmsLzv++rMTQgghKiLD7IUQQtxQRo8ezcaNGxkzZgw333wzHh4eAEybNo0WLVrUezydOnVi2bJlTJgwgQkTJvDKK6/QunVrfHx8SEtL4+jRo1y+fJl77723zNZ1FRk6dChOTk6sWLGC3r17ExkZiVarpVevXkycOLHGMc+ePZtp06bRsmVLWrVqhYODA/Hx8daV7ceNG0enTp2s5UeOHMn777/P1KlTue2222jWrBktWrTAzc2N5ORkDh8+TFJSEi+88EKpnQAWLVrE8OHD2bVrF5GRkfTs2ZPAwEASEhI4evQoycnJqKpqLT9r1iz27dvH+vXradWqFQMGDMDFxYWdO3cSExODl5cX3333Xa1t/bZ06VL69+/P8uXL2bJlC71798ZoNLJhwwbat2+Pt7c3O3fuLHXN5s2bef/99/H29qZjx474+vqSnZ3Nrl27SEpKIigoiOeff75W4hNCCHF9k2ReCCHEDeWxxx4jOzubZcuWsXLlSusK5WPGjLFJMg9w991307VrVz744APWrl3L9u3bMZvN+Pn50axZMyZPnlyl3mQ/Pz9WrVrFa6+9xv79+9m5cycWi4WioqJaSebnzZvH+vXr2bt3L5s3byY3Nxd/f3+GDBnCI488Umr/9xJPPfUUAwcO5MMPP2Tjxo2sX78ejUaDv78/HTt25NZbby2zQJ6HhwebN2/m888/56uvvuLQoUPs2LEDX19fOnTowKhRo0qVNxgMrF69mgULFvDFF1+wdetWCgoKCAkJ4cknn+SFF16olfnyJVq3bs2+ffuYOXMmq1atYsWKFQQHB/Pkk08yY8aMcrcwnDBhAg4ODmzbto0TJ06wefNm3NzcCA0N5ZlnnuGRRx4pd6s7IYQQ4u8U9a+vtIUQQghxwwsPD+fSpUtcvHix3O3VhG0sXryYiRMnMn78eBYvXmzrcIQQQtiY9MwLIYQQolzPPfcczs7OtGnThmnTptk6nBvWww8/jMlk4ty5c7YORQghRAMiybwQQgghyvXDDz8AMGjQIEnmbWjp0qWlFtgTQgghQIbZCyGEEEIIIYQQjY5sTSeEEEIIIYQQQjQykswLIYQQQgghhBCNjCTzQgghhBBCCCFEIyPJvBBCCCGEEEII0chIMi+EEEIIIYQQQjQykswLIYQQQgghhBCNjCTzQgghhBBCCCFEIyPJvBBCCCGEEEII0chIMi+EEELcQCZMmICiKCxevNjWodRITEwM//3vf7nzzjsJCwvDYDDg7OxMVFQUL730EsnJybYOUQghhKhTOlsHIIQQQghRVQ888ADbt29Hp9PRsWNHevToQVpaGrt37+bNN99k4cKFrFmzhg4dOtg6VCGEEKJOSDIvhBBCiEYnKCiId999l7Fjx+Ll5WU9npyczD333MOmTZu45557OHnyJFqt1oaRCiGEEHVDknkhhBBCNDrffPNNucd9fHxYunQpISEhnD17lp07d9K7d+96jk4IIYSoezJnXgghhGjkjEYj7733Hr1798bDwwODwUBYWBgjR47kq6++qlQd2dnZLFiwgDvvvJPIyEicnJxwcnKiXbt2vPzyy2RkZJR73ZUrV3j66adp3rw59vb2ODo6EhISwqBBg5g7d26Z8uvWrWPkyJH4+fmh1+vx8PAgMjKSMWPGsGXLlpr8GKyCg4Px9vYGIDY2tlbqFEIIIRoa6ZkXQgghGrHY2FiGDRvGiRMncHR0pFevXnh5eREXF8fWrVs5evQoDzzwwDXrOXz4MI888gg+Pj60aNGCzp07k56ezv79+3njjTf49ttv2bVrV6kh7QkJCXTp0oX4+HhCQ0MZNmwY9vb2xMfHc+jQIfbv389zzz1nLb9kyRImTpwIQLdu3RgwYAB5eXlcvnyZ5cuX4+3tTd++fWv8M0lJSSE9PR2AgICAGtcnhBBCNESSzAshhBCNlMVi4c477+TEiRPcfPPNLFu2DB8fH+v5/Px8NmzYUKm6wsPDWbduHQMGDECj+XPgntFo5LHHHuOLL75gxowZzJs3z3ru008/JT4+nkceeYT58+ejKIr1nMlkKtPTPmvWLFRVZevWrWWGviclJREXF1el+6/I3LlzMZvNBAQE0LNnz1qpUwghhGhoZJi9EEII0Uj9+uuv7Nu3j4CAAH744YdSiTyAvb09w4cPr1RdwcHBDBo0qFQiD+Do6Mgnn3yCTqfju+++K3UuMTERgGHDhpVK5AH0ej2DBg0qU97Nza3cOey+vr507NixUrFezbp166zD+99++23s7OxqXKcQQgjREEnPvBBCCNFIrV69Gijeps3Z2blW6tyxYwdbt24lJiYGo9GIqqoA2NnZkZycTHp6Oh4eHkDxUPmPP/6YF198EVVVufnmm68aR7du3di0aRPjxo3j6aefpmPHjmVeHtTE0aNHufvuuzGbzTz55JPcf//9tVa3EEII0dBIMi+EEEI0UpcuXQKgZcuWNa4rKSmJ0aNHs23btquWy8rKsibzY8eOZe3atXz55ZeMHj0arVZL69at6d27N3fddRcDBw4sde3HH3/MiBEjWLp0KUuXLsXFxYWuXbsycOBAxo4dS2hoaLXjP3XqFIMHDyYjI4OJEyfy/vvvV7suIYQQojGQYfZCCCGE4KGHHmLbtm306NGDNWvWkJiYSGFhIaqqoqqqdSG5kp56AI1Gw7Jlyzh+/DhvvfUWI0aM4MqVK3zyyScMGjSI2267DbPZbC3fqlUrTp8+ze+//87UqVNp27YtW7du5ZVXXiEyMpJly5ZVK/YzZ84wcOBAkpKSGDduHAsXLiwz7F8IIYS43kgyL4QQQjRSJT3Zp06dqlE9ubm5rFy5Eo1Gw8qVKxkyZAi+vr7o9Xrr+YSEhAqvb926NdOmTWPFihUkJSWxbt06fH19+fXXX/niiy9KldXpdAwfPpy5c+eyY8cOUlJSmDlzJoWFhTz66KPk5uZWKfazZ88yYMAArly5wpgxY1i0aFGtDt0XQgghGir5104IIYRopIYNGwbA119/XeUk+K8yMzMxm824urri7u5e5vyyZctK9chfjaIoDBo0yLod3qFDh65a3tXVlVdffRV3d3eMRiNnzpypdNznz59nwIABxMfHM2bMGJYsWSKJvBBCiBuG/IsnhBBCNFK33XYbHTt2JD4+nrvvvpvU1NRS5/Pz81m1atU16/Hz88PDw4OMjAyWLl1a6tyuXbuYPn16udd98cUX7N+/v8zx7OxsNm3aBEBYWBhQvMXdO++8Q3JycpnyW7duJSMjA61WS3Bw8DXjBbh48SIDBgwgLi6OsWPHSiIvhBDihiML4AkhhBCNlEaj4aeffmLo0KGsWrWK0NBQevfujZeXF3FxcRw+fBh3d3eio6OvWo9Wq2XGjBk8++yzjBs3jnnz5hEREUFMTAw7duxgzJgxbNmyxbrgXokff/yR8ePHExgYSIcOHfDw8CA9PZ3t27eTmZlJ27ZtefjhhwEoLCxk6tSpTJs2jXbt2hEZGYleryc6Oppdu3YB8PLLL5fZXq8io0ePJjY2FoPBAMCDDz5YbrmHHnqo3K3whBBCiMZOknkhhBCiEQsLC2Pfvn18/PHHfP/99+zcuZPCwkL8/f3p16+fdbj7tTzzzDM0adKEt956ixMnTnD8+HFatmzJvHnzmDRpEk2aNClzzdSpU2nSpAk7duzgwIEDpKWl4enpSevWrXnggQeYOHEiTk5OADg7OzN//nw2b97MwYMHWbt2LYWFhQQGBnLnnXfy+OOPl1n9/mrS0tIAKCgoKDOa4K/69+8vybwQQojrkqJWdhKcEEIIIYQQQgghGgSZXCaEEEIIIYQQQjQykswLIYQQQgghhBCNjCTzQgghhBBCCCFEIyPJvBBCCCGEEEII0chIMi+EEEIIIYQQQjQykswLIYQQQgghhBCNjOwz/z8Wi4X4+HhcXFxQFMXW4QghhBBCCCGEuAGpqkp2djaBgYFoNBX3v0sy/z/x8fGEhITYOgwhhBBCCCGEEILY2FiCg4MrPN8gk/ktW7YwZ84c9u/fz5UrV/jpp58YNWrUVa/ZtGkTU6ZM4fjx44SEhPDKK68wYcKESrfp4uICFP/AXF1daxC9EEIIIYQQQghRPVlZWYSEhFhz1Io0yGQ+NzeXqKgoHnzwQe68885rlr948SK33norkyZN4ssvv2T9+vU89NBDBAQEMHTo0Eq1WTK03tXVVZJ5IYQQQgghhBA2da3p3w0ymb/lllu45ZZbKl1+/vz5NGnShLfffhuAVq1asW3bNt59991KJ/NCCCGEEEIIIURj0SCT+arauXMngwcPLnVs6NChPPPMMxVeU1BQQEFBgfX7rKysugpPNDImi4kDiQfYGLuRTbGbyCvKo7t/d3oF9aJXUC+8HbxtHaIQQohKsJjNHJszFLDQ5rk1aHXXxcceIYQQArhOkvmEhAT8/PxKHfPz8yMrK4u8vDwcHBzKXPPmm28ya9as+gpRNAJn0s+w+NhiNl/eTFZh6Zc7q6JXsSp6FQCtPFsxJGwIIyJGEOAcYItQhRBCVELMmYO0z98LwPkTe2javqeNIxJCCCFqz3WRzFfH9OnTmTJlivX7kkUGxI0nqzCLjw99zPJTyzGrZgA8DB70C+nHgJABuBnc2B63ne3x2zmReoKTaSc5mXaSDw5+QDf/boxsOpIhYUNw0jvZ+E6EEEL8VeKxzYT/7/+nnNgsybwQQojrSq0k85mZmezdu5fk5GTCwsLo2bN+/7H09/cnMTGx1LHExERcXV3L7ZUHMBgMGAyG+ghPNFAW1cLP537mvQPvkZafBsDg0MGMaT2GDj4d0Gq01rKd/TrzVKenSMlLYevlrfx24Tf2JOyxfv1nz3+Y3Ws2g8IG2ep2hBBC/I3m8h7r/9fF7bZhJEIIIUTtq3gH+krIzs7moYcewtfXl6FDhzJmzBgWLlxoPb9w4UICAwPZvbtu/wHt0aMH69evL3Vs7dq19OjRo07bFY2X0WTkn3/8kxk7ZpCWn0YTtyb8d8h/eXfAu3T261wqkf8rbwdv7oi8g8+GfsYfo//gqY5PEe4aTo4ph2c2PcO8Q/OwqJZ6vhshhBDl8c86Yv3/IdmHUS3y97MQQojrR7WT+by8PPr378/nn3+Oh4cHt9xyC6qqliozYsQIEhMTWbFiRZXqzsnJ4dChQxw6dAgo3nru0KFDxMTEAMVD5MeNG2ctP2nSJC5cuMDzzz/PqVOn+Pjjj/n222959tlnq3t74jqmqiozd8xkX+I+HHWOTO08lR9G/kDPwKqNKAl0DuTh9g/z0+0/MabVGADmH57P0xueJqcwpy5CF0IIUUlpSXGEqPEAmFQtvqRx5dIZG0clhBBC1J5qJ/PvvPMOBw8e5P777+f8+fP89ttvZcr4+/vTqlUrNm7cWKW69+3bR8eOHenYsSMAU6ZMoWPHjsyYMQOAK1euWBN7gCZNmvD777+zdu1aoqKiePvtt1m4cKFsSyfKtej4IlZHr0an6Phk8CdMaDsBvVZf7fp0Gh0vdHuB2b1mY6exY9PlTTyw8gGiM6NrL2ghhBBVEnN4MwDRmhDO65sDEHd4/dUuEUIIIRqVas+Z/+abb/D39+ezzz7D3t6+wnLNmzdnz549FZ4vT//+/cv08v/V4sWLy73m4MGDVWpH3Hi2xW3jvf3vATC9+3Q6+XWqtbpvb3Y7Td2b8vTGp7mYeZH7f7+f//T9D32D+9ZaG0IIISon78J2AJLcorAY3CDhJGrMTuAJ2wYmhBBC1JJq98yfP3+ebt26XTWRB3B0dCQlJaW6zQhRa2KyYnh+y/OoqIyOHM3dze+u9TbaerflmxHf0Mm3EzmmHCavn8yCIwuu+nJKCCFE7XNP+d8L/tDu2DfrA4Bfhrz0F0IIcf2odjKv1WoxmUzXLHf58mWcnGTLLmFbuaZcnt74NNmF2UT5RPFS95dQFKVO2vJ28GbhzQu5t8W9qKh8cPADpm6eitFkrJP2hBBClFZYkE9EYfH8+IC2/QjvMACAMMtl0pLibBmaEEIIUWuqncw3bdqUw4cPU1RUVGGZnJwcjhw5QqtWrarbjBA1VmAuYOqmqZzLOIePgw/v9n8XO61dnbap1+p55aZXmNljJjqNjrWX1vKPlf8gyZhUp+0KIYSAi0d3YFBMpONCcNN2uHv7E60JBeDSoaqt4yOEEEI0VNVO5m+77TauXLnC7NmzKywze/ZsMjMzueOOO6rbjBA1UmguZMqmKWyP346DzoH3BryHj6NPvbV/V/O7WDR0Ed4O3pzLOMczG5+hwFxQb+0LIcSNKP30NgAuObZF0RR/1En0KF5Ut+D8NpvFJYQQQtSmaifzzz77LEFBQbz++uuMGjWKr776CoDExER+/PFH7rvvPubMmUN4eDiTJk2qtYCFqCyT2cTUzVPZcnkL9lp75g2aR3uf9vUeRwffDnwx7Atc7Vw5mnKU/9v1fzKHXggh6pBd/F4A8vw6W49pw4q3H/VM3W+TmIQQQojaVu1k3t3dndWrV9OkSRN++eUXxo4di6IorF69mrvvvptvv/2W0NBQfv31V5kzX4ssZjO7v/kPqYmXbR1Kg2aymJi2ZRqbYjdh0Br4cNCHdPXvarN4QlxDmNN3DhpFw0/nfmL56eU2i0UIIa5nqsVCaO4RANxa9LEeD4oqnjffxHQeY06mTWITQgghalO1k3mA1q1bc+zYMT7++GNuvfVWWrVqRYsWLRg8eDDvvPMOx48fp3Xr1rUVqwCOb/+N7iffwOXj9hyYO5IjG7/HfJV1C25EZouZF7e8yPqY9dhp7PhgwAfcFHCTrcOiZ1BPnu30LABv7XmLvQl7bRyREEJcf67EnMWbDEyqloj2va3HA8JakIA3esXMhUObbRihEEIIUTtqlMwD2NvbM2nSJH755ReOHTvGiRMn+OOPP3jmmWdwdHSsjRjFXygKnNE1x04x0ylnC+03/5Pk2S3YtfglikyFtg6vQZi7by5rLq1Br9Hz7oB36RnU09YhWY1vM55bmtxCkVrEc5uf40rOFVuHJIQQ15X4o5sAuKhvir2jc6lzl12iAMg+I/PmhRBCNH41TuZF/Wrb53aav7KXC3etYZfP3WTihD8p3BQ9j31fvmrr8Gzu29PfsuzkMgDe7PMmfYP72jii0hRFYVbPWbT0bElafhqTN0wmJS/F1mEJIcR1w3xpFwBpnh3LngspHqXlnLinXmMSQggh6oIk841URNvu3PTEQgwvnGVnk8kAtLn4OenJN25P7874nbyx+w0Anuz4JEPDh9o4ovI56Bx4f8D7eNp7cib9DGNXjiUmK8bWYQkhxHXBO/0QAPrwHmXO+bbpB0DT/BMymk0IIUSjp6iVXFY7IiKi+o0oCufPn6/29fUhKysLNzc3MjMzcXV1tXU4VWIxm7nwRleamc+zy/cebnp8ga1DqncXMi8w5vcxZJuyGRExgjd6v4GiKLYO66ouZV3i0bWPEpcTh6e9J58M/oTWXrLGhBBCVFdOVjoObzdBq6gkP3IYn8DwUuctZjM5r4fgSi5nb/+VyI4Na/SWEEIIAZXPTXWVrTA6Orrc44qiVLjNVsm5hp5UNXYarZa8fjNhwzg6Jf5A3IUpBEW0snVY9SY9P53J6yeTbcqmo29HZvWc1Sj+zIW5hrFs+DIeW/cYp9JOMXH1RN4b8B49Asv2JgkhhLi26MNbaKuoXMGHgL8l8lD87+VFh7ZE5e0m9eRmSeaFEEI0apUeZn/x4sUyX1OmTAHg9ttv58cff+TgwYMcPHiQn376iVGjRgEwZcoULly4UCfBiz+163s7R+w7Y6eYSfjpJVuHU2+SjEk8uvZRYrNjCXIO4r0B72GntbN1WJXm7eDNoqGL6O7fHWORkcfXP86Wy1tsHZYQQjRK2ed2ABDn0r7CMnl+XQDQJRyul5iEEEKIulLpZD4sLKzU1+HDh3n33XdZtmyZNXmPiooiKirKmtx/+eWXvPvuuxw8eLAu70H8j9Pw2VhUhc7ZGzh78PpPCE+lneKB3x/gZNpJPAwefDTwIzztPW0dVpU52znz8eCPuTnsZoosRbyw5QWiM6NtHZYQQjQ69snF+8sXBZRd/K6EzjscAIf8xPoISQghhKgz1V4Ab+7cuXTp0oX777+/wjL33XcfXbp04e23365uM6IKmrbvyX73IQAUrnoF1WKxcUR1Z1PsJsatGkeiMZEmbk348tYvaebRzNZhVZud1o5/9/k3nXw7kWPK4dlNz2I0GW0dlhBCNCo+ecUjAZ1Doyos4+gdCoCbKaleYhJCCCHqSrWT+SNHjhAZGXnNcpGRkRw5cqS6zYgqCr5zNoWqjjaFhzmy+Qdbh1PrVFXli+Nf8NSGp8gryqN7QHeWDV9GiEuIrUOrMb1Wz9x+c/F28OZcxjlm7JhR4XoUQgghSjPmZBJoKe5tD2zeucJy7n7hAPhYUq/rl95CCCGuf9VO5i0WS6VWqD9//rwkJPUoIKwFB/zvBsB52xvX1QcVVVX54OAHzNk3BxWVu5rfxSeDP8HVrnHtPnA1Po4+vNP/HXSKjj+i/+CLE1/YOiQhhGgULp85iEZRScEdT9+gCst5/29hPINiIiNVhtoLIYRovKqdzHfs2JE9e/bw008/VVhmxYoV7N69m44dK567Jmpfq3tmUaDqaWq+wPmjO20dTq1QVZV397/LwqMLAZjSeQozbpqBXqO3cWS1r6NvR6Z1nQbAu/vfZW/CXhtHJIQQDV9mdPGCdgmG8KuWszPYk4I7AKnxskCvEEKIxqvayfy0adNQVZV77rmHf/zjH6xcuZKTJ09y8uRJVq1axZgxY7jnnntQFIVp06bVZsziGty8/Dju0hOAlB1LbRxNzamqytx9c1l0fBEA07tNZ2LbiY1i+7nqur/l/YyMGIlZNTN101ROpp60dUhCCNGgmROOA5Dj1uKaZdO13sVlk2PqNCYhhBCiLlU7mR8xYoR1Ybvly5czcuRI2rZtS9u2bRkxYgRfffUVqqryn//8hxEjRtRawKJyNFH3AdA0cTXmoiIbR1N9qqry1t63rMPNX+n+Cg+0esDGUdU9RVH4V49/0carDekF6Tz4x4PSQy+EEFfhlHkGAK1/m2uWzTH4AVCQFlunMQkhhBB1qdrJPMCzzz7LgQMHePDBB2natCkGgwGDwUBERAQPPvgg+/bt47nnnqutWEUVtO57Jxk440M6J3b8butwqsVkNjF712yWnVwGwIweM7i35b02jqr+OOgcWHDzAjr7dSbHlMOktZPYELPB1mEJIUS9iM+Jx2Q2Vbp8QMFFANzCK17JvkShoz8AlozL1QtOCCGEaABqlMwDtGvXjgULFnDmzBmMRiNGo5GzZ8+ycOFCoqKu/Q+qqBt2BntOew0GIH//VzaOpuoSchOY8McEvj3zLQoKr/Z4lbub323rsOqdi50L8wfPp39IfwothTy76Vl+OlvxOhVCCHE9WHZiGUN/GMq9v99LRn7GNcunJcXhTQYWVSG4+bXX6VFdixfI0+VeqWmoQgghhM3UOJkXDZdb9zEAtM7YRF5uto2jqbxtcdu4+9e7OZJ8BBe9C+8NeI/RzUfbOiybsdfZ827/d7m96e1YVAszdsxg0bFFtg5LCCHqxNenvuY/e/8DwNn0szy67lGyC6/+b1j82QPF/9X44ejsds02dB7BADjmJdQwWiGEEMJ2JJm/jrXoMoh4xQ8nJZ/jm5bbOpxrMlvMfHTwIx5f9zgZBRm08mzFNyO/YWDoQFuHZnM6jY7Xe73OhDYTAHhn/zu8ve9t2fZRCHFd+fb0t7yx+w0ARkeOxsPgwYnUEzyx/gmMJmOF1+XEHAEg2SGiUu04eocC4GZKrmHEQgghhO3oqnvhwIGVT7AURWH9+vXVbUpUk6LREBM0gsDLn6E/9h3c+rCtQ6rQ2fSzzNwxk6MpRwG4p/k9PN/teQxag40jazgURWFql6l42nvyzv53WHx8MRkFGczsMROdptq/ykII0SD8cOYHXt/1OgAT2kxgSucp3NfyPh7840EOJh3kqY1PMW/QvHL/XdAknQAg37Nlpdpy928CgLclBdViQdFI34YQQojGp9oZwKZNm65ZRlEUVFW9rrcQa+gC+46Drz6jjXEvaUlxePoG2TqkUkxmEwuPLuTTo59SZCnCWe/MKze9wq0Rt9o6tAZrYtuJuBvceXXnq6w4t4LMgkze6vsW9jp7W4cmhBDV8uv5X5m1cxYAY1qNYUrnKSiKQkvPlswfPJ+H1zzM7iu7mbJpCu8NeA+9Rl/qerfscwDYBVx7JXsAr4Dinnl7xUR6aiIePgG1eDdCCCFE/ah2Mr9x48Zyj1ssFi5dusRvv/3Gjz/+yPTp07n55purHaComdDmHTija07zojOc3fAF3e+bbuuQrA4lHWLWzlmcyyj+ENY/pD+vdH8FPyc/G0fW8N0ReQduBjembZ7GxtiNTFo3ibf7vY2Xg5etQxNCiCrJLMhk9q7ZqKjc3/J+nu/6fKlOgPY+7flo0Ec8tu4xtlzewjenvmFM6zHW8xazmWBTNCjgHXHtxe8ADPaOpOKGF5mkXbkoybwQQohGqdrJfL9+/a56fsKECXzwwQc8//zz3HPPPdVtRtSCtKaj4PRbuJ/7CbBtMm8ym1hzaQ1fn/qaw8mHAfC092R6t+kMDR8qoziqYGDoQOYPmc9TG55if+J+7vr1Lt7s8yY3Bdxk69CEEKLSlp5YirHISAuPFrzY7cVy/x3o6t+V57s+z+u7XuezY59xV/O7rKOREmLPEajkU6jqCGzattLtpml98DJnkp0cA/SsrdsRQggh6k2dThJ76qmnCAkJ4dVXX63LZsQ1NBswjiJVQ4ui08SeO2qTGHIKc5h3aB5Dvh/Ci1tf5HDyYXQaHXc0u4MVt69gWJNhkshXQ1f/riy9ZSnN3JuRkpfCI2se4YMDH1BkKbJ1aEIIcU1ZhVl8efJLACZFTUKjVPyx5I5mdxDgFEBKXgo/nP3BejzpXPFK9pe1wejtKr/OSo7BF4CC1JjqhC6EEELYXJ2v+BIVFcW2bdvquhlxFd7+IRx36AzA5S1L6739IksRkzdMZv7h+aTmp+Lj4MPjHR5n7V1rea3Xa3jYe9R7TNeTZh7N+OrWr7ir+V2oqCw4uoCJqycSnxNv69CEEOKqvjz5JTmmHJq5N7vmziV6rZ6H2j0EwGdHPyO/KB+A/MvFL6nTnJtVqe1CR38ALJlxVQ1bCCGEaBDqPJlPS0sjJyenrpsR11DQ4jYAfOPW1nvbHxz4gP2J+3HSO/FW37f4464/eCzqMbwdvOs9luuVg86BmT1mMqffHJz1zhxKPsToX0bz09mfZPs6IUSDlFOYw9ITxS+YH4169Kq98iXuaHYH/k7+JOclW3vndamnADB5t65S+xbX4r3mdTlXqnSdEEII0VDUaTK/ZcsWtm7dStOmTeuyGVEJkb3vxqwqNDVfID76dL21uz5mPYuOLwLgtZ6vcUuTW8qsQixqz7DwYXw38juifKLIMeUwY8cMntzwJMlG2UtZCNGwfH3qa7ILs4lwi2BI6JBKXaPX6nm4XfE2q58d/YwCcwFeucWLqDoGV24le2td7sW7uzjmJVTpOiGEEKKhqPYCeK+99lqF57Kzszl58iR//PEHFouFhx56qLrNiFri4RPAcUM72hQeIWbHtwSG/6vO24zJiuGVba8AMLb1WG4Ol10N6kOwSzBLhi1hyYklfHTwIzZf3syon0fxcveXuaXJLbI2gRDC5nJNuSw5sQSAR9o/glajrfS1dzS7gwVHF5CQm8A3J5Zzv/kyKODXrHOVYnD0CQPA1SQvO4UQQjRO1U7mX331Ves+8hXRaDQ8/fTTPPPMM9VtRtSi7PChcOYIrtF/AHWbzOcX5TNl0xRyTDl09O3Is52frdP2RGlajZYH2z5In6A+vLztZU6mneSFrS/wzelveLbzs3Tw7WDrEIUQN7BvTn9DZkEm4a7hDAsfVqVrS3rnX9/1Op8dXci9GjPZFgf8gqs2CtDNLxwAH0syqsWCoqnzmYdCCCFErap2Mj9jxowKe/js7OwICgpi4MCBBAcHVzs4UbvCet0NZ+bQouAYaUlxePoG1Vlb/7f7/zidfhpPe0/m9psrQ+ttJNIjki9v/ZKFRxfy2dHPOJB0gLGrxjIwZCBPd3qaCPcIW4cohLjBGE1Glhwv7pV/uP3DVeqVLzGq2Sg+PfIpicZEfnB2plN+KC2rmIx7Bxb3zNsrJjLSknD39q9yHEIIIYQt1ahnXjQuAWEtOKdtSjPzec5v+x7PO5+uk3b2JuxlxbkVaBQNc/rOwdfRt07aEZWj1+h5LOox7mh2B58c/oQV51awIXYDmy5vYkTECCa0mUCkR6StwxRC3CC+Of0NaflpBDsHM7zJ8GrVYae14+F2DzN792w+c3cl1Fj1tXkM9o6k4oYXmaReiZZkXgghRKNT7TFlMTExpKWlXbNceno6MTGyh2tDkRxcvMiQ/uzKOmtj8fHFANwVeRfdArrVWTuiavyd/JnVcxY/3fYTA0MGYlEt/HL+F+785U4mrZ3EzvidsvK9EKJOZRVmsfDoQqB4BXudptp9CtwReQceZg1JOh07PO2qVUe6tnhXleyk6GrHIYQQQthKtZP5Jk2aMG3atGuWe/7554mIkKG8DYV/97sAaGXcT252Rq3Xfz7jPFsub0FBYVybcbVev6i5CPcI3h/4Pl/f+jU3h92MRtGwPX47j6x9hLt+vYsvjn9BQq6s7iyEqH2fH/2crMIsmrk3Y2TEyBrVZae1476MAgBWcw6TxVTlOrINfgAUpMbWKBYhhBDCFqqdzKuqWulePOntazjCW3bmsuKPQTFxettPtV7/Fye+AGBg6EDCXMNqvX5Re9p6t+Xt/m/z+x2/849W/8BB58CZ9DPM2TeHId8PYfyq8Xx18isuZF4gqzBLfo+FEDWSmJvIspPLAHi609PVmiv/VzlZ6UzMTsDTbCa5KJ3fL/xe5ToKHYuH1lsy42oUixBCCGEL1R/fVknZ2dnY2VVv+JuofYpGw2W/QQQnfInl5G9wy8RaqzvZmMyv538FYEKbCbVWr6hbwS7BvNjtRR6LeozfL/zOH9F/cCDpgPWrhE6jw9PgiaeDJ572nnjYe+Bp71nmq+R4kaWIHFMOOYU55Jhy0Gl0tPNuh0aRFaOFuBF9cvgTCswFdPTtSL/gfjWuL/roNtqqKqMzi1jgqWXh0YWMjBhZpZcEFpdASAFdTnyN4xFCCCHqW50l8xaLhePHj7NhwwZCQ0PrqhlRDe6d7oCVX9I8aweFBfnYGexrVF9K/CVijm5hq3caJouJDj4dZOuzRsjN4MYDrR7ggVYPkJCbwJroNay5tIbzGefJMeVQZCkiKS+JpLykarcR6hLKfS3v4/Zmt+Nq51qL0QshGrILmRf46VzxaLBnOz9b4W44VZF9Yh0AHdW2uBmSuJR1iTWX1nBLk1sqXYfeIwQugkN+Yo3jEUIIIepblZJ5rbb02+4lS5awZMmSa173z3/+s2pRiTrVvPNAUla6400GR3etpF2/O6tdV15uNvkLbqYliTweUbwiuvTKN37+Tv6MazPOuu5BgbmA9Px00vLTrF/p+emk5qeSlvfn92n5aaTmp1JgLp7Haq+1x0nvhIudCyl5KcRkx/DW3rf48OCHjIwYSbeAbhi0Buw0dui1ehz1jkS4ReCgc7Dl7QshatmHBz7EolroH9Kfjr4da6VOz8SdANhHDGRMEx3zDs3j0yOfMjR8aKVHADl4F3c2uBVW/yWlEEIIYStVSub/OmdWUZSrzqHV6/UEBwczevRoZs2aVf0IRa3TaLWc9+yLd9ovGI/8DDVI5g8vmcJNagJfujqTa8kn1CWU/iH9ay9Y0SAYtAb8nfzxd7r21k2qqpJXlIdeq0ev0VuPG01GfrvwG1+f+ppzGef49sy3fHvm2zLXaxQNEW4RtPJsRWuv1oS6huKsd8bZztn6Xxe9S6307AkhKlZoLiSjIMM6VSbHlANAlE8UTnqnStdzOPkw62LWoVE0PN2xdrZEzUxPoZnpDCgQ2uUWWvj6sOT4Es5lnGNj7EYGhQ6qVD3u/sVru3hbUlAtFpQq7lUvhBBC2FKVknmLxWL9/xqNhgkTJvD555/XelAl5s2bx5w5c0hISCAqKooPP/yQbt3K3+ps8eLFTJxYev63wWAgPz+/zuJrzOzb3Qabf6Fp6iby83Kxd6j8B7MSJ3atplvidxQpsNS1eMj0uNbjaryokWjcFEXBUe9Y5rij3pF7WtzD3c3vZl/iPr4/8z0JuQmYLCYKzYUUWgrJLMgkLT+NcxnnOJdxjl8v/FpuGzpFV2rOvoudS3EPv9YOvUaPvc6eFp4t6BnYE097z7q+ZSEaDFVVKVKL0Cm6Ui+8VFUlqzCLhNwEEo2JpOSl4GHwIMw1jCCXIAxaA6qqcjbjLNvjtrM9bjv7k/ZTZCkq04ZOo6Ojb0d6B/WmV2Avmns0r/DlWpGliHf2vQPAbU1vo5lHs1q5zwt7V9NRUYnRBBEaXLzH/P0t72fB0QV8euRTBoYMrNQLP6+AcAAclEIy05Nx8/KrlfiEEEKI+lDtOfMzZ86kY8faGSpXnm+++YYpU6Ywf/58unfvznvvvcfQoUM5ffo0vr6+5V7j6urK6dOnrd9Lz13FWva4ldTNbniTwe7PJtN98qIqXZ+Xm43rH8+gUVS+cGlCnN6Mm9nCbc1uq6OIxfVCURS6+nelq3/Xcs8nG5M5kXqi+CvtBIm5ieSacq2L6RVaCilSi0jOSyY5L/nqbaHQxqsNvYJ60da7LdmF2dZpAhkFGfg6+tLNvxvtfdpj0Brq4navG0WWIuvP39vBu9YWMjSajOxJ2MO2uG1EZ0Xj7eCNv6O/dSSIRbWUmtqRVZiFTqNDr9Fjp7XDTmOHVqNF4c+/7xVFwUHnUDyS43+jOSyqhYTchOIvYwIpxhTcDG6EuoYS6hJKiEsILnYuxOXEEZsdS0xWDLHZsViw4KJ3sU4XcdA5lLl3FzsXQl1CSyXGtc1kMbH3yl72Je4rNb0lLT8NY5GRAnMBhebCUtuz2Wnsin9GWjvyi/IxFhnLrVtBwd/JH7PFXGZNDI2iKfVzzDXlEpcTx96EvexN2Mu7+98lyieKDwZ+UObFmaqqzN41mwNJBzBoDTwe9Xit/TwKz2wA4Ipnd0pW5RnbeizLTi7jROoJfjz7I6Obj75mPfYOTqThiidZpMRfvCGSeVVVSchNICUvpdTUqczCzD9HYRTmkGvKxV7351QpJ70TTd2acmfknfL5SgghGogaJfN16Z133uHhhx+29rbPnz+f33//nc8//5wXX3yx3GsURcHf/9rDgAUY7B051e9tvDY/RPeUHzmwqi+dqrCy/eElU7lJvUISnqxuGgmZp3ggK5vclBQc/EPqMHJxvfNx9KGfYz/6hZS/2vVf5++X/DerMAuT2UShpZACcwG5plz2JezjdPppjqUe41jqsQrbm394PgatgSifKNp4tSGjIINEY6I18csryitVXlEUHHWOpT7gehg88HPyw9/JHz9HP3wdfckvyi/1QdlYZESv0WPQGv5MRLV2pb530Dng51hcj7eDNzpN8V/RqqqSa8q1voQoSdwKzYUUWApABb1Wb11/wKAzEOEWgYudS5V+9vlF+ZxJP8OJ1BOcTDvJydSTJOclk2vKLfVzcNA5EOEWQVP3pjRzb0aoSyheDl7W0RLOemfrGgkxWTHEZMeQUZBhTS71Gj2KonA46XCFvb+NlYKCj6MPdprSu7joNLoy00U8DB6lRph42HtYzznpnbDT2LEvcR+ro1ez7tI6MgoyqhRLoaV4xAt/2X7d3eCOv5M/Xg5epOWlEZMdQ64plyu5V4DidS66+nelV1AvegX2Isw1rEziFpMVw7a4bWyP386eK3s4nHyYCasn8OmQT0tNxfno0Ef8cPYHNIqG//T5DwHOAVX7YV6Ff9puAPSRA63HPOw9+Eerf7Dw6EJe3fkq5zPPM6XzFOvvUUXStD54mrPISY4Gbqq1GBuSnMIcdl/Zzbb4bWyP22593tUR6RFJe5/2tRidEEKI6qrzremqo7CwkP379zN9+nTrMY1Gw+DBg9m5c2eF1+Xk5BAWFobFYqFTp0688cYbtGnTptyyBQUFFBQUWL/PysqqvRtoJKIG3M3OM1voceULIndNJ65FN4Iiyv95/dXJ3X/QLfFbUOBAzxc4fmUeGlXlruwc4k/uxluSeVGHqjJ/P8mYxI74HWyL20ZMVgxuBjdr4uRmcONC5gX2JuwlJS+FPQl72JOw55p1qqpqnT+caKy7FbA1igZvB28UFNLz04uTsipQUGjq3pQOvh3o4NOBAKcA4nLirAl2bHYsuaZcCswF1qkOxiIjFtVyzXrzivI4nnqc46nHKyyjUvGaKn8X5BxE76DetPVuS3p+Ogm5CVzJvUKiMRGdoiu15aGrwRWzxVycqP7vhYZZNZeqz6JayCvKKzPXu+RFSYBTAN4O3mQUZFhfNsRkxZBVmEWwS7C1pz7UNRSdRkduYS7ZpmxyTbkYTcZS96aqKukF6dZ6ck25JBlrZzG1v/8cPe096RfcjyDnIOsWkSUvT0pGKdhp7dBpdBRZijBZTNYXP3qNHj8nvzKLS6qqSlp+GrHZsZgspkqNUgl1DeUB1+KdLy5kXuDRtY9yMfMi41aNY8HNCwhzDeOrk1/x6ZFPAXjlplcYFFa5OeyVkRR3kTDLZcyqQtOuw0qdm9xhMgALjy5k6YmlnEo7xZy+c/By8KqwvmyDHxjPk596udZitLXUvFQOJx/mUPIhDiUd4mjyUYrUP1+a6TQ6fBx8Sr1McjO4lR6Foneg0Fxo/T368eyPxOXEEZ8bL8m8EEI0EJVO5gcOLJ5/tmTJEoKDgxk4cOC1L/ofRVFYv359pcunpKRgNpvx8ys93M3Pz49Tp06Ve02LFi34/PPPad++PZmZmcydO5eePXty/PhxgoODy5R/8803ZWE+oMvEuZycs49WphMkfjmWgmnbMNiXne9cIjc7A+fVxcPr97gP54x3IVyBtiYnfM1mzsccAO6qvxsQ4ip8HX0Z1WwUo5qNqrCMqqpczLzInoQ9nM84j5eDl/Vlgb+jP852zmXKG4uM5BTmFCd4hbmk5qda5yIn5CaQZEzCUe+Ih70HXvZeeNp74qh3xGQ2lVojoCQZLfneaDKSaEwk0ZhYvBXg35JCB50DHgYP7HX21uRNr9WjoFBoKcRkNllHJiQaE61rD3x/5vtK/8w87T1p5dWK1p6tae3VmmCXYOswaye9E4qiEJsda637XPo5EnITSo1AUFHRKBoCnQIJdS1OjL0cvErdv8lioql7U3oH9SbUJfS6GLZbktjH58SXecFQaC4sNV0kx5RTaoeI9Px00gvSyS0sLqP+739uBjcGhw5maPhQuvp3vWYvc1UpioKXg9dVk92riXCL4IthX/DI2keIzopm3Kpx/KPVP/jo4EcAPNHhCe5ufndthsylvSvxBc7rI2nu6VPqnFaj5elOT9PGqw0vb3uZvQl7ufe3e3mp+0uEuYZZk9a/TpUodPQHI1gyGm8yn2vKZdeVXWyP287uK7uJyY4pUybMNYxegb3oFdSLrv5dq7xryKm0U8TlxJGWl1ZbYQshhKihSn8q2LRpE4qiYDQard9XVn18SOvRowc9evSwft+zZ09atWrFf//7X15//fUy5adPn86UKVOs32dlZREScuP1KOvtDHiMW0r6Z/1pZj7P7s+epPsTn5VbNiUhlvQFo4hU40nCk8hx7/Pyhn8AcJNda+AUhuSKhzML0RApikKEewQR7hG2DsXKolpIzSt+QQBYh7BX5cN3Sl4Kh5MPczipuHcuNS+VYJdgQlxCCHMNI8QlBDeDW6l51U56J7zsva75d3YTtyY0cWvCkLAhZc7lF+WTVZiFh8EDvVZfztXXL0VRrL2cNVEysiDXlIuHvUepXSEaogDnABYPW8xj6x7jZNpJPjz4IQD3tbiPR9s/WvsNXtwEQKpvjwqLDA4bTIRbBE9vfJrorGie3vjnKvpaRYuHvQctPFvQwacDdi462qYq6HKqP/S8ulRVpcBcQI4phyJLUakpODpFR1p+mnWdh4TcBDILMktdX2Qp4kjKEQ4mHizV8w7Q1K14ZE6UTxRd/LoQ4lqzzzgeBg8A0gvSa1SPEEKI2lPpZH7jxo0AhIaGlvq+Lnh7e6PVaklMLD2ENTExsdJz4vV6PR07duTcuXPlnjcYDBgMsuAVgH9IMw73fQePLQ/TPfl7di7yJurul3B0drOWiT13FM2Xo4lUE0nHlYzbFpNWcIGE3ARc9C70DLwFLv6Ib+4ZG96JENcHjaLBx9EHH0efaxeugLeDN4NCB1V6i67aYq+zx15nX69tXm80igYnvVOVtn+zNS8HLz4b+hmT10/mQNIBbg67mRe7vVjrL/NVi4WwzH0AuLS6+p/tCPcIvr71a+bum8v+xP3W9TXMqpmUvBRS4lLYHrcdgPfDgvEyn8Lwwy3W6xVFIdQ1lA4+Hejg24F23u1w0DlwNv0sexL2sDdhLweSDqCqqnU0j5+jH94O3phVc6lRN3lFeaWma2QXZltHatTWmhGhLqH0DupNz8CedPDtgJvB7doXVYGnQ/FLqvR8SeaFEKKhqHQy369fv6t+X5vs7Ozo3Lkz69evZ9SoUUDxtnjr169n8uTJlarDbDZz9OhRhg8fXmdxXk+iBt7DzrOb6XFlGT0uzSd17tccafYgUXdMJfb0fnx+GYsHWcQrfpgf+I7mkVG8tPUlAIY1GUZEsz6wCYLVK2RnpuHiJtuBCSHEjcTFzoWFQxdyKvUUrb1a18k2pTFnDhFGGvmqnmZdBl+zvLOdM6/2fNX6vcliIiM/g4TcBI6mHOVQ8iH2xu4kpSiDZJ0FckoPtY/NjrUm/BpFg5POiWxTdpl2sgqzOJNe/ZfZCgo6ja7UbgQlbXo7eFun/XjYe5TZTSHcNbx4uoprKHWppGc+LV+G2QshREPRIBfAA5gyZQrjx4+nS5cudOvWjffee4/c3Fzr6vbjxo0jKCiIN998E4DXXnuNm266iWbNmpGRkcGcOXO4dOkSDz30kC1vo1Hp/tAH7PmlBcFHPiJQTcTr3LukzvmcYDUfR6WAs9pmeDy8Am//EHJNuayLWQcU7x3s4RNAIl74kcrlU3tp1X2oje9GCCFEfdNr9LTzaVdn9V85uJow4Kx9W9o5VH3kgl6jt456aefTjgdaPUDchePovuxDtNYBwz9/R9EUjyYoshRxOu00h5IPcTjpMPG58WSbsnHQOdDJt5N1i00HnUOpofDp+enoNLo/d2/Q6nHUORavO2HnVGqRub9veaiqaql1NVztXGt9nYTqKpk+Ism8EEI0HA3jX4hy3HvvvSQnJzNjxgwSEhLo0KEDq1evti6KFxMTg0bz59vp9PR0Hn74YRISEvDw8KBz587s2LGD1q1b2+oWGh2NVku3O57CdOuj7P1tPkFH5xGoJoICR+y70PSJH3BycQdgTfQa8oryCHcNJ8onCoArDpH45aWSeWE/SDIvhBCilhlitwKQE9ir1ur0CgjH3mzGz5xDpi6g1F7znf0680CrBwBIzE0kNT+VSI/IMusYRHpE1kosiqJY58w3NCXJvAyzF0KIhqPSyfxrr71W7UYUReFf//pXla+bPHlyhcPq/74A37vvvsu7775bnfDE3+jtDHS982lMIyaxd+VCzDnJdL5nOnq7P9cY+OX8L0Bxr3zJnMh8rzZweReaxKM2iVsIIcT1q8hUSLPcg6CAd/vae2Fs7+BEGq54kkVSzOlSyfxf+Tn54edU/rkbgYe9DLMXQoiGptLJ/KuvvoqiKKhq5fcOLilf3WRe2JbezkDXUU+UOX45+zL7EvehoDCy6UjrcbuQjnAZvLLL3z5QCCGEqK5zh7fSUskjCyci2vWs1bov2zfHM38fqae2Edmxb63Wfb0oSeYzCzIxW8x1siaCEEKIqql0Mj9z5sy6jEM0Ir+e/xWA7gHd8Xf6c3cB/xbdYCeEFF2isCAfO4OsaC2EEKJ2pB9bC8A5p0500tXuLMFcvy5waR/6+D21Wu/1xN3gDoCKSkZBBl4OXrYNSAghhCTzomosqoWfz/8MwO3Nbi91LiA0kkyccFNyOXdqP82iam9OoxBCiBuXxWzGL2YVAKbQPrVev0tkL7g0n+DsI7Ve9/VCp9HhbnAnoyCDtPw0SeaFEKIB0Fy7iBB/2pewj7icOJz0TmX2r1Y0Gi7bNQMg/fw+W4QnhBDiOnRg9edEWKLJVh1oMWh8rdcf0aEfRaoGP1JJiDlb6/VfL0qG2ssieEII0TDUWjKvqiopKSkkJydjsVhqq1rRwCw6vgiAW5vcioPOocz5bI/i3QMs8YfrNS4hhBDXJ1NhAf775gJwLHw87t7+17ii6hyd3bioiwDg8pGNtV7/9cK613yBLIInhBANQY2T+bVr1zJs2DBcXFzw8/PD398fFxcXhg0bxh9//FEbMYoG4lTaKbbFbUOjaJjQZkK5ZXRBxdvUuWaerMfIhBBCXK8O/PwhwWoCabjSbvSLddZOqmdHAMzRO+usjcauZGh9Wp4k80II0RDUKJmfNm0aw4YNY82aNRiNRlRVRVVV8vLyWLNmDcOHD2fq1Km1Fauwsc+OfgbA0LChhLiGlFvGq1kXAEILL2Axm+stNiGEENeffGMOTY7PA+BMi0k4u3rUWVv6Jj0A8E4/VGdtNHYlPfPpBTLMXgghGoJqJ/PLli3j7bffxt7enqlTp3LkyBGys7PJzs7m6NGjPPfcczg4OPDee++xbNmy2oxZ2EBMVgxrLq0B4J/t/llhuZDIKPJVPU5KPnEXjtVXeEIIIa5Dh354C1/SuIIPHe94tk7bCokaCEB40UVysiRZLY/MmRdCiIal2sn8hx9+iFarZfXq1cyZM4e2bdvi5OSEk5MTbdq04a233mL16tUoisJHH31UmzELG/j82OdYVAt9gvrQwrNFheV0ejti9E0ASDqzt77CE0IIcZ3Jykil5fniEWGxUU9jsHes0/Z8g5pwBR+0isrFQ1vqtK3GytPeE4C0fBlmL4QQDUG1k/ljx47Ru3dv+vSpeIuYkvPHjkkPbWOWZEzil/O/APBQu4euWT7dtSUAhZdlETwhhBDVc/z72biTwyVNCJ1HPlYvbca5Fq/7knNuW72019hIMi+EEA1LtZN5e3t7AgMDr1kuMDAQOzu76jYjGoClJ5Zispjo5NuJTn6drn2Bf3sAnNKP13FkQgghrken920gKvZLAFK7v4BWp6uXds1B3QBwTpTtVcsjw+yFEKJhqXYy37lzZ44cOXLNckeOHKFLly7VbUbYWGZBJt+e/ha4+lz5v/Jo1hWAsPxTFJkK6yw2IYQQ15fMtGR2fziOyF/vxFEp4JSuFR2H/KPe2vdu3Q+AJvknMRcV1Vu7jYX0zAshRMNS7WT+5Zdf5uTJk7z11lsVlpkzZw4nT57kpZdeqm4zwsa+PvU1xiIjzT2a0yeo4ikVfxXRrieZOOFGLmcPyH69Qgghrk61WNi74iPMH3Sie+rPaBSVvW7D8Hv0JxRNjXfRrbTwVl3IVh1wVvKIPrGn3tptLEp65jMLMjFbZMcaIYSwtWqPW1MUhcmTJzN9+nS+++47xo4dS5MmxQufXbx4kWXLlrF//36eeuopNBoNW7aUXkymb9++NYtc1Ln0/HSWnlgKwD/b/hNFUSp1nU5vx1nXHnTJWkfGoV+g+9C6DFMIIUQDUViQT8ypfejs7LF3dsfJ1RMnZzcKC/NJvRJNxpVojCmXKMq4jCYrDkNeIi4FiXiZk+hKDgDRmhCMQ+bQtcct9R6/VqfjokNr2ufvJ+XkFpq271nvMTRk7gZ3AFRUMgoyrPvOCyGEsI1qJ/P9+/dHURRUVWX//v0cOHCg1HlVVQH44IMP+OCDD0qdUxSFIhm+1uB9cPADsgqzaOHRgqHhVUzIWwyDvesITNxcN8EJIYRoEAoL8jm5/RcKj/xIi4wtNCO3TBl7IOh/XxUxqgYON32Uzve+jJ3Bvq7CvaZc3y4Qsx/t5dI987FnD5NwfCtth4zHwcnFRtHZlk6jw83gRmZBJmn5aZLMCyGEjVU7me/bt2+le2pF43M89Tg/nPkBgOndp6PVaKt0fWTPOyja8zxhlljiLhwnKKJNXYQphBCiDmRnppEaf4GsxEvkp8ZizriMNicefUE6Cqq1nGIpokn+CaL+ksBn4oQFLU5qLnbKn0Ox81Q7UjTeZNr5kmfvT5FzABr3YOy9gnHxbYJfWAt6uLjX522Wy7lZL4j5L8HZf64LdGjtVzTf9gwhSgFJh97mWIepdB75GBpt1f5tvB542nuSWZBZ7UXwLGYzmWlJuHv51esUCiGEuB5VO5nftGlTLYYhGhKLauHN3W+iojK8yXA6+3Wuch1uHt4cN7SlTeERYnf9JMm8EELYgGqxkJp0mZSY0+QmX8KUfhmy4rAzJqKopec8a815uBYm4W1OwUXJoyp9zym4c957IC6d76FF1yHW1efz83LJyUxDr7fD1cOHEI2GkFq8v7rQpENfzOsV/JVkEi+f5+KGxXQ7/yEaRSVf1eOrpOF76GXOH/2MvAGzaNv7NluHXK88DB5c5CJpBVVbBC8vN5sjv39C4MlFhKjx7Osyhy4jHqmjKIUQ4sZQP3u9iEbltwu/cTj5MA46B6Z0nlLterLDBsPZIzhdWge8UnsBCiFqlWqxkJWRSnZaIvbOrji7emKwd2zwvWbmoiJSE2PJz80sdVyj0eEXGonezlDudarFQlL8RVzcvXF0drtqGxazmbTkeNKvXCQnOQZTVhIagxM6RzfsnNwxOLnj4OyOk6sHji7uFbZZExazGWNuFrlZaRQYs0vfiwpFBUYKcjMpzM2gyJhJUU4KSupZXLLOEWiKxpscvKvS4P8G3WXiRJrGhyyDL/kO/lhcAtE4+6L8baSWc2ALWnQdQvdyto+zd3DC3sGpindsW86uHpzTRdDMfJ7cz+/kJks0KLDbaxRtJ7zPrhXv0Pr8ApqaL8C6sVxe70+m3g+jQ8logxBcgtsQGNkRd29/W99OrbOuaJ/3ZzJvMZvJzcnEmJ1e5s+o2VRAwo6vaRX3Hd3/ty4CgHp2LSDJvBBC1IQk86KUnMIc3tn3DgCToibh5+RX7bqCut8JZ9+hRf4RsjPTcHHzrK0whWiwCgvySY67SEr0YfIuH0ebegb33PM4mbPI1HlhtPfH5BQAroGgKKj5WSgF2SiF2WjMhRQ5+aJxC8bgFYqzTyg6OwP52ekU5mZiysvEXGDE4O6Pq28ongERuLp7UVCQR/SR7WSc3Y7dlf34Gs9SoHEk286XAkd/VJcgFAc3LAU5UJCFpiALbWE29gXJuBUm4WVJxU0p4K9prUnVkqs4YFQcydM4UaBxwqRzwqR3ocjRD9yCsfMMwcU3DBfvQBxdPHB2cbcOOzYXFZGWdJm0+AvkpsRgykzEkp8FBdloCrPRmHLQmXKwK8rBYM7F3mJEqxaRqfMm196v+GfkFgSKFjU/0/oz0hdm4liQjLspCW81DV/FUu5zMKoGzhiak+XdEYcmPdA7upF1bgf2iQcIMx7DjywsqkK8xpck+ybkuTdDcfFHzU5An5uAY14C7kXJeFtS8VaKKp0M56l2GBUHLH/bLKZAMZCp9yHP3g+Tc2BxW/lZaLPjsDcm4FKYhLMlq9Q1CioGtQAn8nFWVJwr+4fwbyyqQqLiTYbeF2PJz9Y1EEVX+sWDorfHwSsUV78wvAPDcXNx5+qvOq5fqR4daJZynghLNGZVYV+rF+h+33QAbhr7GunJj7H721folPQTwSQQXJgAhYchE4gDjgN/FI9YSDCEY3QIxGLnDHYuYO+KxsENg0cALr5N8AoIx9XDp8KXZ6rFQp4xG2NWBiZTPo7/e3mk09vV28/jr1y0xS9njm1dRMx3s/Ewp+FEPi6KWuFojib/+2+c4scVl3Z0yVqHW250fYQrhBDXNUUtWamumvLz89m3bx/x8fHk5+dXWG7cuHE1aabOZWVl4ebmRmZmJq6urrYOx2bm7p3LkhNLCHcN58fbfkSv1deovthZrQhR49nf7T06D59YS1EKUX/MRUVciT5FysUj5MUfQ5t+EUUtnUDqinJwLkjEoygZTzUTjVKjv1arxKga0FFUam5yTeqyp7BG8eeoDuQrBtzUbPS1ENO1FKkajIoDf43YTjXhoBRe9TqzqqCt5H1aVIUUxYN0nQ95eg90lvziFxDmXBxUI06q8Zrt1QaTqiVPsefvURdiR57GkXyNE4VaRwp1rhS4haP3b417WHuCI6Owd6zuq4Ab04E/ltJp52SycOTSgHm063dnueVSEmJJvHCUvJTiKQya7Hjsc2LxzY8mgORKt2dUDWQpLliHRQAKFuzJx0nNQ1fOSyujasCoOJBkF0KmV0fsm3QnNKo/Xn7BmIuKSEm4RHpCNMbkGEzZKaj5WagF2WgKstAUGYuHdfyFxc4ZfFriEtLWOqog35jD5bOHybh0BFPCSVxTDrDOMZaFHq7cm5XNK6ml581X9Gc0zi6Cgs4P037QP4g9e4jwbwYVbwE4M77BjwASQghbqGxuWqNkfs6cObzxxhtkZWVds6zZ3LD3I5VkHk6nnea+3+6jSC3ik8Gf0Duod43r3PXJJG5K/Jq9bkPp+uy3tRClaKiKTIWkXLlERkI0uSmXUYsKSp1XdAacvIJwD2iCl39onQxH/ru4C8e5vPc3DJc24p13sdTCXZWhAB6W9ConagWqnnhtEGlOTSj0bIF9YGvs3f0xpsZjSo+BrHjscq+gKlqK9M6odi6oBhfQaNHkJGKXewXnwiQ8ipLRYMGoFCdqBVpHzBo7nExpeJpT8ODP4aypuBHj2IZ8/y64RHTFXJhPfmoslszL6HKuoDXlYNY7Y9Y7oxpcUAwuaF39cfAOwd2vCd5BTbB3cCo1pDs/O4O83AxMuZkU5mZizsvEYkyD7CvY5SbgXJCAR1EybmpWuS8TzKpCiuJJus6XPIMXRXoXzHpnMLiCvQuKwRWdoxs6RzcMjm6g1WJMiaUwLRYy49AbE4rr0Ttj0TujGlxR7F2x8wjCyScMz8AIPH2DrfOzS1jMZmLPHCLxxFa4vAffzCPYW/KJd25FYUAX3Jv3okm7nhizM4k/e5Cc2KOQfAp9fiqFjn7gGoTeIxgnnzDc/cMr9ee1sCAfY3YGxuwM8nMzUf+2B3eBMQtjSgxF6ZdRsuLQ5yVRpHfF7BKI1i0IB+9QHNx9UZTSiY2dgxMOLh6NZurD9cJiNnPwj8UEtO5NYHiLatWRm51B3P8SYXNWgnVUirYwG70pC6fCFLzMyXhw7c9QUPz7VIQOg2K6arl0XHFRc8p9AVAVGTjjquaWebn3pasz//bypLPJg2fCJuEV3gYHF0+cXT0q9We0IN+I7s1AtIpKyqSjePuH1ihOIYS4HlU2N632MPuPPvqIF154AYB27doRGRmJi8uNuVXL9eBYyjEeW/cYRWoR/UP610oiD+DSfiSs/ZpmmTsxFxWV+dAtGociUyEpCTHFiXrJIlqZcdgZr+CUX9wj7aWm46+oVGaGqFlVSFI8SNEHku3aDHxa4hTSFt/wtqWGjqqqSkbSZdKjD2NKOIF9+hk88mPR/m3hLrOiK+6V1Dlh0jmjavT4Zx8lWE246lZYlaIUJ+eXdSGkO0Vg8mhWdniynSMGz1CcfcPwDAjHwzuAJhqNdWhpTVU0QSUvN5uU+Ato9fYEhEbiVQuJnkarxdnVA2dXj0pfo1os5BfkkZuVjjE7nUJjNi5eAXj6BuGnt6P6k3WqR6PVEtaqM2GtSi/e+fc/mwZ7Rzx8AoDhNW7TzmCPncH/upwjfSPSaLV0Hv7PGtXh5OJO8079oFO/q5bLN+aQEh9NXnbZBeXsHF1xdHEv/nJyxaDRlHpxZMxKIe3CAYjZjW/mEULNsXgoWaAU95KnKp6k633I13tQpHfBYueMxeAKds5ldyTKTcEh44x1VIE7OaAUJ/Xx+vDiv6v92qKEucOpDyA4gg5DHqjyz8Vg78hljR/BagKJ5yWZF0KImqhRMq/T6fjhhx8YOXJkbcYk6tmeK3t4csOTGIuMtPNux+s9X6+1upt3HUzWWic8yOLUgY207Dak1uoWdevc4W1krfkPwbnHKpeoK1CoaknReJGp86ZIUzrh1VkKcCtKwduSip1ixpc0fE1pkHoMUoFT5VdbqV2MVcACFJU+bFK1nDG0JiuoH27Ne6OpxhxTJ3dfAsNb0bQBvohycHIhJDLK1mGgaDTWhc68/IJtHY4QjYq9ozPBzdpWunyZF0dRf758z8pIJenSKdx8gvD0DcZfp6vUC9a/y8lKJzH6JG6+wXj5BuP+1xeFV3bDKaq9NR1Aqn0owXkJ5MSdAG6tdj1CCHGjq/an0+joaPr27SuJfCO3IWYD0zZPo9BSSHf/7rw/8H2c9LW38rDezsBhl5vokr2e9EO/gCTzDd65w9vJ/mM2HY07/jxYKlH3IdfenyInf+siaM4+oXgGNsHTJ4hArZbAq9RvMZtJSY4nLf4CWZdPFve4Z5zFJ+8CgZbEMkM6M3EiXh9OlmszVO8WOAW2QmtwLF2nqQBTXjZFxuKh4GphDg6BbWnW/RbaVKGHWQghGjNXdy9c3XvVuB5nVw+c2/cs95yHffHfqWn5Vdua7q/yXJtC3h7UlLPVrkMIIUQNknlfX198fHxqMxZRz349/yv/2v4vzKqZgSEDeavfWxi0dTCPuflQ2L+egIRNtV+3qDUXT+wl47cZ1iTerCocdBuMc+9H8Q5tUalEvTI0Wi3e/iF4+4dcc/gpgNv/voQQQtheydZ0mQWZmC1mtH/bqrAyND7NIREcsy7UdnhCCHFDqXYyf8stt/DHH39gsVjQyII8jU5cThwzdszArJq5reltzOo5C52mboYRR/YcRdG+Fwm3xBB/8RSBTVrWSTuielISYrjw7Ut0Tv2NJor6vyR+ED63/osuLTrYOjwhhBANiLvBHQAVlYyCDLwcKjUZqhTnoFZwDLwLLtVydEIIcWOpdhY+c+ZMCgsLeeqppygsrPsteUTtWnBkAUWWIrr7d+f1Xq/XWSIP4OblxxlDGwBif3+rztoRVWPMyWTn58/j+EkXuqX9ilZROeDUl8sPbKLLlB8Ik0ReCCHE3+g0OtwMxeOlqjvU3i+iHQD+lmTyjTm1FpsQQtxoqp3BBQYGsm3bNm677TZatGjBgAEDCA0NLbeXXlEU/vWvf9UoUFF74nLi+PnczwA80fEJNErdj6wo7Po4bH+M7ik/sPvblnS/5/k6b1OUT7VYOLB6CSF7XqMHaaDAaV0L1CGz6dT9ZluHJ4QQooHztPcksyCz2ovgefoEkokTbkou8ReOE9G2ey1HKIQQN4ZqJ/OqqvL+++9z6tQpLBYLixcvLlNGURRUVZVkvoFZcGQBRWoRNwXcREffjvXSZochD7Ar7hg3Rc+j8/E3ObIxgvYD7qqXtsWf4i4cJ/Xbp+icvw+AeMWXK11eoNMtD8r+1UIIISrFw+DBRS6SVlC9nnlFoyFBF4Jb0SnSY06AJPNCCFEt1U7m58yZw4cffohOp2PEiBFERkbi7Oxcm7GJOhCfE2/tlX8s6rF6bbv7uNns/eACXTNWEbFpMhd9Q2jSRv4Brw8F+UYOfPUqnS59TpBiolDVcSBkPB0eeI1AR/m9FUIIUXkli+Cl5VV/Rfss5yaQcYrCxAr2JRVCCHFN1U7mFy5ciKOjI1u3bqVjx/rp3RU1t+Boca9894DudPLrVK9tKxoNUY8t5vjbQ2hTeISc7x4gxWMD3oFh9RrHjSY5PpqMz++iR9FZUOCYoQNud33ATQ1gf3IhhBCNT8n2dOkF1d9rvsgzEjJWoU8/V1thCSHEDafa42pjY2Pp06ePJPKNSHxOPCvOrgDqv1e+hJ3BnuBHvydWCcSfFPIWDGPfL/MxFRbYJJ7r3dlDW1E/HUBk0VnScWFf17m0eWEjIZLICyGEqKaSnvnqzpkHsPdvAYBbbnRthCSEEDekaifz/v7+uLi41GYsoo4tPLrQ2ivf2a+zzeJw8/JDM/Z7UnEjRI2ny4EXSH+jJbu++BeZack2i+t6s3/lIoJ/uhNf0ojWhGAct5Yutz4sc+OFEELUSEnPfHVXswfwDGsLQFBRLKrFUitxCSHEjaban+rvuOMOtm7dSn5+fm3GI+pIfE48P537CbBdr/xfBUW0QTt5DzvDJpGCO76kcdOFD9C/34bd8/5J3IXjtg6x0VItFnYueoHOe57BQSnksH1XvJ7eQlBEK1uHJoQQ4jpgnTNfg2Q+ILwlRaoGR6WApPiLtRWaEELcUKqdzL/66qt4enpy//33k5KSUpsxiTqw8OhC677ytuyV/yt3b396TPwPLi+eZE/UbC5qwnFUCuie/D0BS3pxcM6tnNz9h7yxr4KsjFQOzR1Bj0vzAdjldx9tn1uNi5unjSMTQghxvaiNYfZ2BnuuaPwBSL54rFbiEkKIG021F8B75plnaNGiBStWrGDDhg107tz5qvvMf/bZZzUKVFTflZwr1l75SVGTbBxNWQZ7R7rd8STq7U9wdNuvWHZ8SFT+XjrmboNV24hf7UeO1o0CrRMmnTNFehdU//YEdRlJcLO2tg6/wbh4Yi+678bRUY2nUNVxqP0r3DT6WVuHJYQQ4jpTG8PsAVIcwgkxxpMbfxK4vRYiE0KIG0u1k/nFixejKAoA2dnZbNq0qcKykszbVkmvfDf/bnTx72LrcCqkaDS063s79L2dSyf3k7j2XaJSVxNIIhQlQhFQsk5exko49W/iFD8ue/bA0HIoLXvfjr2Dky1vwWb2/fYprfe+gqNSQALeZN3+Od069bN1WEIIIa5DJT3zmQWZmC1mtBptteopcG0Cxh2QcrY2wxNCiBtGtZP5RYsW1WYcoo5cybnCj+d+BBpmr3xFwlp1JqzVMjJSEjh35gAmYyZFxgzMeVlYcpJxTdxNZMFxgkgkKHUFbF9BzrYp7HPrhbbtHbTqc8cNkdibi4rYu2AyNyV+DQocNXQi+KGvaO4TYOvQhBBCXKfcDe4AqKhkFGTg5eBVrXq0vi0gARyzLtRidEIIceOodjI/fvz42oxD1JG/9sp39e9q63CqzN3bH3fv4eWey8lK58TuVRScXkt4ymb8lFS6ZK2DHevI2f4cxw3NMTr4U+QcgMYtGAffCJp3G4q9o3M930XdyM/L5cS8+7gpZwsAO4Mm0m3iXLS6av9aCyGEENek0+hwM7iRWZBJWn5atZN5l+DWcAR8CmJqOUIhhLgxyKf+61hCbkKj7JWvLGdXDzoMeQCGPIDFbObU/g1k7PuOJknr8FNSaVN4GAoPQyYQB5wA40YD+117oWk7ilZ9RjfaxD4zLZm4+XfQqfAohaqWI13/TY8Rj9g6LCGEEDcID4MHmQWZNVoEzz+iXfF/ScGYk4mjs1tthSeEEDeEGifzV65c4eeff+b06dNkZWWhqmqZMjJn3jZKeuW7+ndtlL3yVaHRamnZbQh0G4LFbObM4W1kXT6JKT0WTXY8dsYEAoxn8FdS6Jy9AXZuIHfH85zWh6OiWOsxa3RkBfUjcsgjeAeG2fCOKpYQe478RaNobYklW3Xg0pBP6dL7NluHJYQQ4gbiae9JdFY0aQXVXwTP3dufdFzxIIv488doFtWrFiMUQojrX42S+Q8//JBp06ZhMpmsx0qS+ZLF8VRVlWTeBhJyE/jh7A9Aw9hXvj5ptFqad+oHf1sATrVYOH1gE+l7vyU8cR3+SjItik6VreDiUYr++zEHnW5C6Tyetn3vRKe3q6foy2cxmzl3eBuph1fS9NK3hJNGEp7k3L2ctm272zQ2IYQQNx7rXvN5NVvRPkEfgofpOBmxx0GSeSGEqJJqJ/Pr16/n6aefxtXVlalTp7J582Z27tzJf//7X86cOcOPP/5IdHQ0zzzzDFFRUbUZs6iEkl75Ln5drvte+cpSNBpadBkIXQaiWiycObSV3JTYUmUKMxNwO/MDLU0n6GjcAVt3kLF1KpccWmP07YRLs56ER/XF2dWjzuPNzkzj9OZv4ewammbvoTnZ1nOXNCEYJvxERGhkncchhBBC/F3J9nTpBdUfZg+Q7RwO6ccxJZ6phaiEEOLGUu1k/v3330dRFP744w+6d+/OxIkT2blzJw8//DAAs2fP5rHHHuPzzz/nwIED1Wpj3rx5zJkzh4SEBKKiovjwww/p1q1bheW/++47/vWvfxEdHU1kZCT/+c9/GD68/MXTrleZBZl8dvQzfjhT3Cv/eIfHbRxRw6RoNMW99+V6jkunDnBl46e0SPwdD7Jwz9sDl/bApflY1ilkKY7k4ki+xol8rSN5Bm9MoX0J6TqSwCYtqx1XSQKvPfUzrXP30kX5c9RLturAOefOFIYPoM3Qf9bLCwUhhBCiPNZkvgZz5gEsXpGQDnYZ52ojLCGEuKFUO5nfs2cPnTp1onv38of4GgwGPvnkE1auXMlrr73G4sWLq1T/N998w5QpU5g/fz7du3fnvffeY+jQoZw+fRpfX98y5Xfs2MH999/Pm2++yYgRI/jqq68YNWoUBw4coG3bttW5xQYpuzCb02mn8Xfyx8/RD71WD0B+UT5fnvySz459RnZhcQ/u8CbDpVe+msJadiKs5XxMhQWcObaLtNPb0MfvIzD7KAFKMq7k4kouWJLBApiAE1vgxGxilUDivXqgDb8Jz/AoApu2rXCbPNVi4dLpAyQc+B3H2M20yDvyZwKvQIwmiLiAIbi3H06zjv3paGeorx+BEEIIUSHrMPv8mg2zt/dvCefA3XipNsISQogbiqKWt2JdJRgMBu644w6WL18OwCOPPMJnn31GTk4ODg4O1nJ33303u3btIjY2tqKqytW9e3e6du3KRx99BIDFYiEkJIQnn3ySF198sUz5e++9l9zcXH777TfrsZtuuokOHTowf/78MuULCgooKCiwfp+VlUVISAiZmZm4urpWKdb6tDN+J4+sLV61XEHBy8ELf0d/koxJJOUlARDpEckznZ6hT1Af69oFovakJ18hKy2RgtwMCnIyMRnTKUg4hVvcVpoXnkCnWEqVN6sK8ZoA0uyDsSh/vj9TUAkwnsGP1FLlYzRBxAcOw6/HvYS36oqi0dTLfQkhhBCVteriKp7f8jye9p5E+VR/OmVebhYOsVuxoJCtlRFnQoi691DXf9GnU8NePDorKws3N7dr5qbV7pn39PQkNzfX+r2HR/FfwDExMbRo0cJ63Gw2k5qaWub6qyksLGT//v1Mnz7dekyj0TB48GB27txZ7jU7d+5kypQppY4NHTqUFStWlFv+zTffZNasWVWKqyEoshQR4hJCYm4ihZZCUvJSSMlLASDQKZDJHSczvMlwtBqtjSO9fnn4BODhE1DuuayMVM7tXonpzDrcss4QaIrGVTESosYTkhdf7jX5qp4zDlEYQ/oR0OlWQlt0JFQSeCGEEA1YmGvxji9p+WlsjN1Ys8qcHP/3f/JrVo8QQlRCSmb5n8kbo2on86GhoaV629u2bYuqqvz222/WZD4nJ4etW7cSHBxcpbpTUlIwm834+fmVOu7n58epU+WsPg4kJCSUWz4hIaHc8tOnTy+V/Jf0zDd0fYL70Ce4D6qqkpafRoIxgYTcBFRVpW9wX+y0tl11/Ubn6u5Fp6FjYehYoHgYfXJCDAnnDpKXdLHM1o2Ovk2I7Hoz7RvpfvdCCCFuTK29WjN/8Hzic2v+oTgl/iLZV2TOvBCifrSJuMnWIdSaaifz/fr149133yUxMRE/Pz9uvfVWnJyceOmll0hISCA0NJQlS5aQlpbGfffdV5sx1wqDwYDB0HjnHytK8RB7Lwcv2ni1sXU4ogKKRoNPYDg+geG2DkUIIYSoVb2Camkruea1U40QQtxoqp3M33333Rw8eJBDhw4xdOhQPD09eeedd5g0aRLvvPMOULzHfHh4eJWHs3t7e6PVaklMTCx1PDExEX9//3Kv8ff3r1J5IYQQQgghhBCisar2xNyuXbuydu1ahg4daj328MMPs3fvXp5//nkeeugh3nnnHQ4ePIinp2eV6razs6Nz586sX7/eesxisbB+/Xp69OhR7jU9evQoVR5g7dq1FZYXQgghhBBCCCEaq2r3zFekU6dOdOrUqcb1TJkyhfHjx9OlSxe6devGe++9R25uLhMnTgRg3LhxBAUF8eabbwLw9NNP069fP95++21uvfVWli9fzr59+/j0008r1V7JXOasrKwaxy6EEEIIIYQQQlRHSU56rY3naj2Zry333nsvycnJzJgxg4SEBDp06MDq1auti9zFxMSg+cuK3z179uSrr77ilVde4aWXXiIyMpIVK1ZUeo/57OzivdkbwyJ4QgghhBBCCCGub9nZ2bi5uVV4vtr7zF9vLBYL8fHxuLi4NPi92UtW3o+Njb3qvoOiYZPneH2Q53h9kOd4fZDneP2QZ3l9kOd4fZDnWP9UVSU7O5vAwMBSHdh/12B75uubRqOp8hZ6tubq6iq/UNcBeY7XB3mO1wd5jtcHeY7XD3mW1wd5jtcHeY7162o98iWqvQCeEEIIIYQQQgghbEOSeSGEEEIIIYQQopGRZL4RMhgMzJw5E4PBYOtQRA3Ic7w+yHO8PshzvD7Ic7x+yLO8PshzvD7Ic2y4ZAE8IYQQQgghhBCikZGeeSGEEEIIIYQQopGRZF4IIYQQQgghhGhkJJkXQgghhBBCCCEaGUnmhRBCCCGEEEKIRkaS+QZq3rx5hIeHY29vT/fu3dmzZ89Vy3/33Xe0bNkSe3t72rVrx8qVK+spUnE1VXmOCxYsoE+fPnh4eODh4cHgwYOv+dxF/ajq72OJ5cuXoygKo0aNqtsARaVU9TlmZGTwxBNPEBAQgMFgoHnz5vJ3awNQ1ef43nvv0aJFCxwcHAgJCeHZZ58lPz+/nqIV5dmyZQsjR44kMDAQRVFYsWLFNa/ZtGkTnTp1wmAw0KxZMxYvXlzncYqrq+pz/PHHHxkyZAg+Pj64urrSo0cP/vjjj/oJVlxVdX4nS2zfvh2dTkeHDh3qLD5RMUnmG6BvvvmGKVOmMHPmTA4cOEBUVBRDhw4lKSmp3PI7duzg/vvv55///CcHDx5k1KhRjBo1imPHjtVz5OKvqvocN23axP3338/GjRvZuXMnISEh3HzzzcTFxdVz5OKvqvocS0RHR/Pcc8/Rp0+feopUXE1Vn2NhYSFDhgwhOjqa77//ntOnT7NgwQKCgoLqOXLxV1V9jl999RUvvvgiM2fO5OTJk3z22Wd88803vPTSS/Ucufir3NxcoqKimDdvXqXKX7x4kVtvvZUBAwZw6NAhnnnmGR566CFJBG2sqs9xy5YtDBkyhJUrV7J//34GDBjAyJEjOXjwYB1HKq6lqs+yREZGBuPGjWPQoEF1FJm4JlU0ON26dVOfeOIJ6/dms1kNDAxU33zzzXLL33PPPeqtt95a6lj37t3VRx99tE7jFFdX1ef4d0VFRaqLi4u6ZMmSugpRVEJ1nmNRUZHas2dPdeHCher48ePV22+/vR4iFVdT1ef4ySefqBEREWphYWF9hSgqoarP8YknnlAHDhxY6tiUKVPUXr161WmcovIA9aeffrpqmeeff15t06ZNqWP33nuvOnTo0DqMTFRFZZ5jeVq3bq3OmjWr9gMS1VaVZ3nvvfeqr7zyijpz5kw1KiqqTuMS5ZOe+QamsLCQ/fv3M3jwYOsxjUbD4MGD2blzZ7nX7Ny5s1R5gKFDh1ZYXtS96jzHvzMajZhMJjw9PesqTHEN1X2Or732Gr6+vvzzn/+sjzDFNVTnOf7yyy/06NGDJ554Aj8/P9q2bcsbb7yB2Wyur7DF31TnOfbs2ZP9+/dbh+JfuHCBlStXMnz48HqJWdQO+ZxzfbJYLGRnZ8vnnEZq0aJFXLhwgZkzZ9o6lBuaztYBiNJSUlIwm834+fmVOu7n58epU6fKvSYhIaHc8gkJCXUWp7i66jzHv3vhhRcIDAws8wFG1J/qPMdt27bx2WefcejQoXqIUFRGdZ7jhQsX2LBhA//4xz9YuXIl586d4/HHH8dkMskHFxupznN84IEHSElJoXfv3qiqSlFREZMmTZJh9o1MRZ9zsrKyyMvLw8HBwUaRiZqYO3cuOTk53HPPPbYORVTR2bNnefHFF9m6dSs6naSTtiQ980I0QP/+979Zvnw5P/30E/b29rYOR1RSdnY2Y8eOZcGCBXh7e9s6HFEDFosFX19fPv30Uzp37sy9997Lyy+/zPz5820dmqiCTZs28cYbb/Dxxx9z4MABfvzxR37//Xdef/11W4cmxA3tq6++YtasWXz77bf4+vraOhxRBWazmQceeIBZs2bRvHlzW4dzw5NXKQ2Mt7c3Wq2WxMTEUscTExPx9/cv9xp/f/8qlRd1rzrPscTcuXP597//zbp162jfvn1dhimuoarP8fz580RHRzNy5EjrMYvFAoBOp+P06dM0bdq0boMWZVTn9zEgIAC9Xo9Wq7Uea9WqFQkJCRQWFmJnZ1enMYuyqvMc//WvfzF27FgeeughANq1a0dubi6PPPIIL7/8MhqN9Gk0BhV9znF1dZVe+UZo+fLlPPTQQ3z33Xcy+rARys7OZt++fRw8eJDJkycDxZ91VFVFp9OxZs0aBg4caOMobxzyr1gDY2dnR+fOnVm/fr31mMViYf369fTo0aPca3r06FGqPMDatWsrLC/qXnWeI8Bbb73F66+/zurVq+nSpUt9hCquoqrPsWXLlhw9epRDhw5Zv2677TbrCswhISH1Gb74n+r8Pvbq1Ytz585ZX8YAnDlzhoCAAEnkbaQ6z9FoNJZJ2Ete0KiqWnfBiloln3OuH19//TUTJ07k66+/5tZbb7V1OKIaXF1dy3zWmTRpEi1atODQoUN0797d1iHeWGy8AJ8ox/Lly1WDwaAuXrxYPXHihPrII4+o7u7uakJCgqqqqjp27Fj1xRdftJbfvn27qtPp1Llz56onT55UZ86cqer1evXo0aO2ugWhVv05/vvf/1bt7OzU77//Xr1y5Yr1Kzs721a3INSqP8e/k9XsG4aqPseYmBjVxcVFnTx5snr69Gn1t99+U319fdXZs2fb6haEWvXnOHPmTNXFxUX9+uuv1QsXLqhr1qxRmzZtqt5zzz22ugWhqmp2drZ68OBB9eDBgyqgvvPOO+rBgwfVS5cuqaqqqi+++KI6duxYa/kLFy6ojo6O6rRp09STJ0+q8+bNU7Varbp69Wpb3YJQq/4cv/zyS1Wn06nz5s0r9TknIyPDVrcg/qeqz/LvZDV725FkvoH68MMP1dDQUNXOzk7t1q2bumvXLuu5fv36qePHjy9V/ttvv1WbN2+u2tnZqW3atFF///33eo5YlKcqzzEsLEwFynzNnDmz/gMXpVT19/GvJJlvOKr6HHfs2KF2795dNRgMakREhPp///d/alFRUT1HLf6uKs/RZDKpr776qtq0aVPV3t5eDQkJUR9//HE1PT29/gMXVhs3biz337uSZzd+/Hi1X79+Za7p0KGDamdnp0ZERKiLFi2q97hFaVV9jv369btqeWE71fmd/CtJ5m1HUVUZZyaEEEIIIYQQQjQmMmdeCCGEEEIIIYRoZCSZF0IIIYQQQgghGhlJ5oUQQgghhBBCiEZGknkhhBBCCCGEEKKRkWReCCGEEEIIIYRoZCSZF0IIIYQQQgghGhlJ5oUQQgghhBBCiEZGknkhhBBCCCGEEKKRkWReCCGE+Ivo6GgURSE8PNzWodhMeHg4iqJYvwYPHmzrkBql/v37oygKmzZtqlE99vb2pZ7HhAkTaiU+IYQQjZsk80IIIW4oJYlqdHS0rUOpM6+++iqKovDqq6/WqJ7Ro0czfvx4hg4dWjuBiWoZO3Ys48ePp1evXrYORQghRAOis3UAQgghREMSFBTEyZMn0ev1tg7F5ubOnXtDj1BoKBYsWADA4sWL2b59u42jEUII0VBIMi+EEEL8hV6vp2XLlrYOQwghhBDiqmSYvRBCiBvC4sWLURSFS5cuAdCkSZNS85BL5jVfbc58SVmAZcuW0a1bN5ydnfHx8eH+++8nJiYGAFVV+eijj+jQoQNOTk54e3szYcIEkpKSKozvzJkzPProozRt2hR7e3vc3Nzo27cvy5Ytq9J9KorCrFmzAJg1a1adzbXev38/9957L8HBwdjZ2eHq6kpERASjR4/m559/rvCaf/zjH4SGhmIwGPD09GTo0KGsXLmywnaKior4/PPPGTx4MN7e3hgMBoKDgxk8eDAffvhhueXnz59Pz549cXNzw97ensjISJ566ini4uLKbeOvz/WHH36gd+/euLq64uTkRK9eva4aX2xsLA8++CABAQHWtl5++WXy8vIqvCYzM5NXXnmFdu3a4eTkhMFgIDAwkF69ejFjxgxMJlOF1wohhBAlpGdeCCHEDaFZs2aMHz+e77//ntzcXEaPHo2zs7P1vL+/f6Xrmj59OnPnzqVv377ccsst7Nmzh+XLl7N9+3YOHz7MpEmT+OWXX+jfvz8RERFs376dJUuWcPDgQfbu3YudnV2p+r777jvGjRtHfn4+LVu2ZPjw4WRmZrJ7927Gjh3Lhg0b+PzzzysV2/jx4zl06BCHDx8mKiqKDh06WM/17t270vd4NevXr+eWW27BZDIRFRVFjx49MJvNxMXF8fvvv2M2m7n99ttLXfP+++8zZcoULBYLHTp0oHv37iQkJLBp0ybWrFnDrFmzmDFjRqlrMjMzGTFiBNu2bUOv19OzZ08CAwNJSEjgyJEjrF+/nieffNJavqCggBEjRrBu3Trs7e0ZMGAArq6u7Nixgw8//JCvv/6aP/74g06dOpV7XzNnzuT111+nZ8+eDB8+nFOnTrFjxw5GjBjBDz/8wB133FGq/KlTp+jXrx9JSUkEBARw2223kZuby7vvvsvGjRvLbcNoNNK7d2+OHTuGj48PgwYNwsnJiYSEBGt7U6ZMwd3dvRpPRgghxA1FFUIIIW4gYWFhKqBevHix3PMXL15UATUsLKzMOUAFVC8vL/XQoUPW40ajUe3du7cKqO3atVObNm2qRkdHW88nJyerzZo1UwF12bJlpeo8cuSIajAYVHt7e/WHH34odS46Olpt166dCqhLliyp9D3OnDlTBdSZM2dW+pq/utbPaMCAAeXei6qqakZGhrpz585Sx1avXq0qiqJ6e3urmzdvLnXuyJEjanBwsAqomzZtKnXuzjvvVAG1Y8eOZWIxmUzqihUrSh174YUXVEBt2rRpqfKFhYXqP//5TxVQmzRpohYUFJS6ruS5uru7q7t27Sp1ruRn2bx58zL32rVrVxVQ77nnHjUvL896/NKlS2rTpk2t9W7cuNF6bsmSJSqg3nLLLWphYWGp+sxms7pp06Yy8ZVYtGiRCqjjx48v97wQQogbiwyzF0IIIarotddeIyoqyvq9g4MDU6ZMAeDo0aN88MEHhIWFWc97e3vz2GOPAcW92n/1f//3fxQUFDB79mzuvPPOUufCwsL47LPPAPjggw/q5F6qIzExEYDhw4eXOefm5sZNN91U6tjMmTNRVZX58+fTt2/fUufatWvHO++8A1Bq2Pzhw4f58ccfsbe359dffy0z7UGn05Xq/c/Pz2fevHkAvPvuu6XK6/V6PvjgA/z8/Lh48SLff/99uff12muv0b1791LHpk+fjpubG2fOnCE2NtZ6fPv27ezduxcnJyc+/vhj7O3tredCQ0OZO3duuW2U/OyGDBlSZpFFjUZDv379yozcEEIIIcojybwQQghRReUlsZGRkUBxknnzzTdXeD4+Pt56zGKxsGrVKgDuvffectvq0qULzs7OHDx4kPz8/BrHXhu6desGwD/+8Q+2bdtGUVFRhWVTUlLYs2cPDg4OjBw5stwy/fv3B2DHjh3WY6tXrwbg1ltvJSgo6Jox7du3j5ycHDw9Pcttx9HRkfvuuw+gwiHw5V1nMBiIiIgAKDXnvmSNhWHDhuHl5VXmuttvvx03N7cyx7t27QrAW2+9xRdffEFaWto17kwIIYQonyTzQgghRBWFhoaWOVYy/z4gIACdruySNC4uLgClEvLU1FSysrIACAkJKbVYXcmXRqMhJycHi8VCampqXdxOlb355pt06tSJVatW0adPH1xdXenduzevvPIKJ0+eLFX24sWLqKpKXl4eBoOh3Hv09fUFIDk52XpdyUKFld1ZoCTRbtKkSYVlmjZtWqrs35X3XAFcXV2B0s/u8uXLV22vokUU+/fvzwsvvEBSUhLjx4/H29ubFi1a8OCDD/Lzzz9jsVgqjF8IIYT4K1kATwghhKgijabid+FXO/d3f03cxo8ff83yBoOh0nXXJX9/f/bt28fmzZtZt24d27dvZ/fu3Wzfvp033niDN998kxdeeAH48x6dnZ0ZPXq0LcO+pqo8u5r497//zaRJk/j111/Ztm0b27dvZ9GiRSxatIiuXbuyceNGnJyc6iUWIYQQjZck80IIIYSNeHt74+DgQF5eHnPnzsXb29vWIVWaoij079/fOkQ+Pz+fxYsX88QTT/DSSy9x11130bRpU0JCQqzlP//880onzCW95KdOnapU+ZKh+BcvXqywzIULF0qVrYmSOqKjoyssUzK6oDzh4eE8+eST1tX49+7dy5gxY9i7dy9vvfWWdXtBIYQQoiIyzF4IIcQNpWRxsavN864vWq2WIUOGAPDtt9/WWr22uEd7e3smTZpE+/btsVgsHDlyBIDAwEDat29Pdna2dR58ZQwbNgyAlStXllpnoCIlawukpaXxyy+/lDmfl5fH8uXLARgwYECl46hIv379gOK5/eXNe//ll1/IyMiodH1du3bl8ccfB+DQoUM1jk8IIcT1T5J5IYQQN5Tg4GAAjh8/buNIis2cORM7OzumTZvGkiVLyp0zfezYMX788cdK11nX9zh37lxiYmLKHD916hRnz54FKLWa/+zZswGYOHEiv/76a5nrVFVl9+7drFmzxnqsQ4cO3H777eTl5XH77beXaa+oqKhU0m5vb88TTzwBwNSpU0v1iptMJp5++mkSEhJo0qQJd911V3Vuu5Q+ffrQqVMncnJyeOKJJygoKLCei42N5bnnniv3up9++oktW7aUec4mk8n6suOvPzshhBCiIjLMXgghxA1l9OjRbNy4kTFjxnDzzTfj4eEBwLRp02jRokW9x9OpUyeWLVvGhAkTmDBhAq+88gqtW7fGx8eHtLQ0jh49yuXLl7n33nvLbF1XkaFDh+Lk5MSKFSvo3bs3kZGRaLVaevXqxcSJE2sc8+zZs5k2bRotW7akVatWODg4EB8fb13Zfty4cXTq1MlafuTIkbz//vtMnTqV2267jWbNmtGiRQvc3NxITk7m8OHDJCUl8cILL5TaCWDRokUMHz6cXbt2ERkZSc+ePQkMDCQhIYGjR4+SnJyMqqrW8rNmzWLfvn2sX7+eVq1aMWDAAFxcXNi5cycxMTF4eXnx3Xff1drWb0uXLqV///4sX76cLVu20Lt3b4xGIxs2bKB9+/Z4e3uzc+fOUtds3ryZ999/H29vbzp27Iivry/Z2dns2rWLpKQkgoKCeP7552slPiGEENc3SeaFEELcUB577DGys7NZtmwZK1eutK5QPmbMGJsk8wB33303Xbt25YMPPmDt2rVs374ds9mMn58fzZo1Y/LkyVXqTfbz82PVqlW89tpr7N+/n507d2KxWCgqKqqVZH7evHmsX7+evXv3snnzZnJzc/H392fIkCE88sgjpfZ/L/HUU08xcOBAPvzwQzZu3Mj69evRaDT4+/vTsWNHbr311jIL5Hl4eLB582Y+//xzvvrqKw4dOsSOHTvw9fWlQ4cOjBo1qlR5g8HA6tWrWbBgAV988QVbt26loKCAkJAQnnzySV544YVamS9fonXr1uzbt4+ZM2eyatUqVqxYQXBwME8++SQzZswodwvDCRMm4ODgwLZt2zhx4gSbN2/Gzc2N0NBQnnnmGR555JFyt7oTQggh/k5R//pKWwghhBA3vPDwcC5dusTFixfL3V5N2MbixYuZOHEi48ePZ/HixbYORwghhI1Jz7wQQgghyvXcc8/h7OxMmzZtmDZtmq3DuWE9/PDDmEwmzp07Z+tQhBBCNCCSzAshhBCiXD/88AMAgwYNkmTehpYuXVpqgT0hhBACZJi9EEIIIYQQQgjR6MjWdEIIIYQQQgghRCMjybwQQgghhBBCCNHISDIvhBBCCCGEEEI0MpLMCyGEEEIIIYQQjYwk80IIIYQQQgghRCMjybwQQgghhBBCCNHISDIvhBBCCCGEEEI0MpLMCyGEEEIIIYQQjYwk80IIIcQNZMKECSiKwuLFi20dSo1cvnyZ559/niFDhhAeHo6LiwsGg4HQ0FDuu+8+tm3bZusQhRBCiDolybwQQgghGp1Tp04xZ84c9u/fT3BwMMOGDWP48OE4OjryzTff0KdPH9566y1bhymEEELUGZ2tAxBCCCGEqKp27dpx8OBB2rdvj0ZTum/i66+/ZuzYsUyfPp2RI0fSqlUrG0UphBBC1B3pmRdCCCFEo+Pn50eHDh3KJPIA999/P/369cNisbBu3TobRCeEEELUPUnmhRBCiEbOaDTy3nvv0bt3bzw8PDAYDISFhTFy5Ei++uqrStWRnZ3NggULuPPOO4mMjMTJyQknJyfatWvHyy+/TEZGRrnXXblyhaeffprmzZtjb2+Po6MjISEhDBo0iLlz55Ypv27dOkaOHImfnx96vR4PDw8iIyMZM2YMW7ZsqcmPoRSdrnjwocFgqLU6hRBCiIZEUVVVtXUQQgghhKie2NhYhg0bxokTJ3B0dKRXr154eXkRFxfHkSNHcHd3Jzo62lp+woQJLFmyhEWLFjFhwgTr8W3bttGnTx98fHxo0aIFQUFBpKens3//flJTU2nWrBm7du3Cy8vLek1CQgKdO3cmPj6e0NBQOnbsiL29PfHx8Rw/fhyz2VzqJcCSJUuYOHEiAN26dSM8PJy8vDwuX77M4cOHmTx5Mu+9916Nfya///47d9xxB1qtlpMnTxIeHl7jOoUQQoiGRubMCyGEEI2UxWLhzjvv5MSJE9x8880sW7YMHx8f6/n8/Hw2bNhQqbrCw8NZt24dAwYMKDV03Wg08thjj/HFF18wY8YM5s2bZz336aefEh8fzyOPPML8+fNRFMV6zmQylelpnzVrFqqqsnXrVnr37l3qXFJSEnFxcVW6/xKPP/44RqORnJwczpw5w9GjR3FxcWHRokWSyAshhLhuSc+8EEII0Uj9/PPPjBo1ioCAAM6cOYOzs/M1r6moZ/5qjEYjbm5ueHh4kJSUZD3+xBNP8PHHH/Pjjz9yxx13XLMeJycn9Hp9hUP2q8vd3Z3MzEzr9z4+PsyfP58777yzVtsRQgghGhLpmRdCCCEaqdWrVwPwwAMPVCqRr4wdO3awdetWYmJiMBqNlLzzt7OzIzk5mfT0dDw8PIDiofIff/wxL774IqqqcvPNN181jm7durFp0ybGjRvH008/TceOHctdwK6qSl4OpKWlcezYMd544w1Gjx7Nfffdx7Jly9BqtTVuQwghhGhoJJkXQgghGqlLly4B0LJlyxrXlZSUxOjRo9m2bdtVy2VlZVmT+bFjx7J27Vq+/PJLRo8ejVarpXXr1vTu3Zu77rqLgQMHlrr2448/ZsSIESxdupSlS5fi4uJC165dGThwIGPHjiU0NLRG9+Dp6Unfvn3p06cPI0eOZPny5fTq1YvJkyfXqF4hhBCiIZLV7IUQQgjBQw89xLZt2+jRowdr1qwhMTGRwsJCVFVFVVUCAgIA+OvsPI1Gw7Jlyzh+/DhvvfUWI0aM4MqVK3zyyScMGjSI2267DbPZbC3fqlUrTp8+ze+//87UqVNp27YtW7du5ZVXXiEyMpJly5bVyr0oimKdQvDTTz/VSp1CCCFEQyPJvBBCCNFIlfRknzp1qkb15ObmsnLlSjQaDStXrmTIkCH4+vqi1+ut5xMSEiq8vnXr1kybNo0VK1aQlJTEunXr8PX15ddff+WLL74oVVan0zF8+HDmzp3Ljh07SElJYebMmRQWFvLoo4+Sm5tbo3sp4eTkBFBqjr8QQghxPZFkXgghhGikhg0bBsDXX39doyQ4MzMTs9mMq6sr7u7uZc4vW7aMyq6XqygKgwYN4oEHHgDg0KFDVy3v6urKq6++iru7O0ajkTNnzlQ1/HKtX78egObNm9dKfUIIIURDI8m8EEII0UjddtttdOzYkfj4eO6++25SU1NLnc/Pz2fVqlXXrMfPzw8PDw8yMjJYunRpqXO7du1i+vTp5V73xRdfsH///jLHs7Oz2bRpEwBhYWFA8Yr47/w/e/cdHkW1PnD8u7vZbHpI740AoUPoTXoRKSoqKlK9qFz1WlDsV0T9qRcVK+oVBVFEFEWvSlFEeu+9JyG9957szu+PIQshfUkICe/nefZJMnNm5mxG4r5zznnfBQtISUmp0H7r1q1kZmai0+nw9/evsb+glsU7ffp0he0lJSV8/vnnfPjhhwA8+OCDtTqfEEII0dRIAjwhhBCiidJqtfz888+MGjWKtWvXEhgYyIABA3BzcyMuLo7Dhw/TokULoqKiqj2PTqfj5Zdf5sknn2Tq1KksXLiQli1bEh0dzY4dO5g8eTJbtmwxJ9wrs2rVKqZNm4avry9du3bFxcWFjIwMtm/fTlZWFh07duSBBx4AoLi4mKeeeoo5c+bQqVMnWrdujV6vJyoqil27dgHw4osv4uHhUav3vnz5ch566CFCQ0Pp0KEDDg4OJCUlcfz4cRITE9Fqtbz55puMGjWq7r9YIYQQogmQYF4IIYRowoKCgti3bx+ffPIJP/74Izt37qS4uBhvb28GDRpknu5ekyeeeIKQkBDmz5/PiRMnOH78OG3btmXhwoXMmjWLkJCQCsc89dRThISEsGPHDg4cOEB6ejqurq60b9+eSZMmMWPGDPPadQcHBz777DM2b97MwYMHWb9+PcXFxfj6+jJhwgQefvjhCtnvq/PMM88QFhbG7t272blzJxkZGdja2hIYGMjtt9/OrFmz6Ny5c63PJ4QQQjQ1GqW2i+CEEEIIIYQQQghxXZA180IIIYQQQgghRBMjwbwQQgghhBBCCNHESDAvhBBCCCGEEEI0MRLMCyGEEEIIIYQQTYwE80IIIYQQQgghRBMjwbwQQgghhBBCCNHESJ35i0wmE/Hx8Tg6OqLRaBq7O0IIIYQQQgghbkCKopCTk4Ovry9abdXj7xLMXxQfH09AQEBjd0MIIYQQQgghhCAmJgZ/f/8q91+XwfyWLVt4++232b9/PwkJCfz888/cdttt1R6zadMmZs+ezfHjxwkICOCll15i+vTptb6mo6MjoP7CnJycrqL3QgghhBBCCCGEZbKzswkICDDHqFW5LoP5vLw8unTpwv3338+ECRNqbB8ZGcmYMWOYNWsW3377LRs2bGDmzJn4+PgwatSoWl2zbGq9k5OTBPNCCCGEEEIIIRpVTcu/r8tgfvTo0YwePbrW7T/77DNCQkJ49913AWjXrh3btm3jvffeq3Uw35TkleSRW5yLl73XpY2KArLWXwghhBBCCCFuCM0im/3OnTsZPnx4uW2jRo1i586dVR5TVFREdnZ2uVdTsCZiDcNWDuPdfeqDC0oK4ZdH4J3WkHK6cTsnhBAN6LfD8XR99U8mfLKdt/84xbazqRQUGxu7W0IIIYQQjaJZBPOJiYl4eXmV2+bl5UV2djYFBQWVHvPmm2/i7OxsfjWV5HctW7QkrySP9RfWk5xyApaOhUPLIC8Fjv3U2N0TQogG8/uReDLzSzgQncnCjeeZ/OVuOs/7g6dXHkZRlMbunhBCCCHENdUsgnlLPP/882RlZZlfMTExjd2lWmnr2pZunt0oVUpZ+dNEiN17aWfU9sbrmBBCNLDodPXh7PR+wUwI98PH2YYSo8KP+2NJySlq5N4JIYQQQlxb9bJmPisri71795KSkkJQUBD9+vWrj9PWmre3N0lJSeW2JSUl4eTkhK2tbaXHGAwGDAbDtehevbvXuT0Hkg+wUm/iQdeW6Ee9Ad/dowb2JYWgt2nsLgohRL1SFIWY9HwAJvcJopWnA4qiMOjtTUSn5xOZmoenk/ztE0IIIcSN46pG5nNycpg5cyaenp6MGjWKyZMn88UXX5j3f/HFF/j6+rJ79+6r7mh1+vbty4YNG8ptW79+PX379m3Q6zaKg98ybP1beJaWkmal48+Rz0Gbm8HeE4xFELe/sXsohBD1LiO/hNyiUgD8XdSHtBqNhmB3ewAiU/MarW9CCCGEEI3B4mC+oKCAwYMHs3jxYlxcXBg9enSFNYtjx44lKSmJX375pU7nzs3N5dChQxw6dAhQS88dOnSI6OhoQJ0iP3XqVHP7WbNmERERwTPPPMOpU6f45JNP+OGHH3jyySctfXvXL/+e6A2O3OXQCoDlEb+qWeyD+6v7L8hUeyFE81M2Ku/tZIONXmfe3rIsmE+TYF4IIYQQNxaLg/kFCxZw8OBB7r33Xs6fP8/vv/9eoY23tzft2rVj48aNdTr3vn37CA8PJzw8HIDZs2cTHh7Oyy+/DEBCQoI5sAcICQlh9erVrF+/ni5duvDuu+/yxRdfNMuydHi0gVnbuXPcYqy0VhxJOcLx1OMQdDGYj9rWuP0TQogGEH0xmA90tSu3PdhN/TkyRYJ5IYQQQtxYLF4z//333+Pt7c2XX36JjU3V6xTbtGnDnj176nTuwYMHV5uZ+Kuvvqr0mIMHD9bpOk1WiwDcgVHBo1gdsZrlp5bzf20mq/ti9kBpMVhZN2oXhRCiPpUF8/6u5fOghHg4ABAlI/NCCCGEuMFYPDJ//vx5evXqVW0gD2BnZ0dqaqqllxHVmNR2EgDrIteR7ugJdm5QWgDxN8hDDSHEDSOmipH5EDd1mn1UWj4mk5SnE0IIIcSNw+JgXqfTUVJSUmO72NhY7O3tLb2MqEYn9050cOtAsamYn86ugqCLVQQuyFR7IUTzUtU0ez8XW/Q6DcWlJuKzChqja0IIIYQQjcLiYD40NJTDhw9TWlpaZZvc3FyOHDlCu3btLL2MqIZGo2FSO3V0/vvT31MaeDGYl3rzQohmpqpgXqfVmLdJRnshhBBC3EgsDubHjx9PQkICr7/+epVtXn/9dbKysrj99tstvYyowajgUbjauJKUn8QOB3XtKDG7wVj1QxYhhGhKSowm4jPVUfcrg3mAEPeL6+YlmBdCCCHEDcTiYP7JJ5/Ez8+P1157jdtuu43ly5cDkJSUxKpVq7jnnnt4++23CQ4OZtasWfXWYVGeQWdggN8AAE4Y88CmBRTnQsLhxu2YEEJU40DSAbKKsmrVNj6zAJMCBistHo6GCvtD3NUAP0KCeSGEEELcQCwO5lu0aMG6desICQnh119/ZcqUKWg0GtatW8ddd93FDz/8QGBgIL/99pusmW9goS1CAYjIipR180KI697B5INMWzeNhzc8XG3lkjIx6ZdG5TUaTYX9MjIvhBBCiBuRxcE8QPv27Tl27BiffPIJY8aMoV27doSFhTF8+HAWLFjA8ePHad++fX31VVQh1FkN5s9nnb+s3rysmxdCXJ+OpBwxf92TWHPp0qrWy5cJdpc180IIIYS48VhcZ76MjY0Ns2bNkqn0jahli5YARGVFUdqtj3pTo3eCyQhaXaP2TQghrhSZFWn+fsnxJfT26V1t+7JgPqCKYL7lxZH5mIwCSowm9Lqrek4thBBCCNEkyCeeZsDPwQ8bnQ3FpmLiHFzB4ARF2ZB4tLG7JoQQFURlR5m/3x63ndPpp6ttH1NDMO/lZMBWr8NoUsxthRBCCCGaOwnmmwGtRkuIcwgA57IjIbCPuuOCTLUXQlx/orKiAMx/t5YeX1pt+5qm2Ws0GoLd1dwsUWky1V4IIYQQN4ZaT7Nv2bKlxRfRaDScP3/e4uNFzUJbhHIy/SQRmREMC+oPZ/9U1833faSxuyaEEGY5xTmkFaYB8FLvl/jHn/9gbeRaHuv2GN723pUeU1MwD2pG+5MJ2USk5DG0bf33WwghhBDielPrYD4qKqrS7RqNpspsxGX7Kss+LOpXWUb781nnIfgOdWPs3kbskRDihlSYBXsWQcc7wDWkwu6yUXkPWw96+fSil3cv9iTu4ZsT3zCn55wK7bPyS8gqKAEgwNW2ysuGyMi8EEIIIW4wtZ5mHxkZWeE1e/ZsAG699VZWrVrFwYMHOXjwID///DO33XYbALNnzyYiIqJBOi8uaemszpyIyIwANzWwJy8ZSosasVdCiBvO6qfg79dg8/xKd5etlw92DgZgeofpAPx45keyi7MrtI/JUEfl3R0M2FlX/fw52E0N5iWjvRBCCCFuFLUemQ8KCir386+//sp7773HsmXLuPfee8vt69KlC7feeisrVqzgvvvuY8CAARWOF/WrVYtWAERkRWA0OKHTGcBYBDkJ4BLcuJ0TQtwYorbD0ZXq9xlRlTYpy2Qf7BQMwAC/AbRq0Ypzmef44fQPzOw0s1z7GPMU+6pH5QFaelwcmU+VBHhCCCGEuDFYnADvnXfeoUePHhUC+cvdc8899OjRg3fffdfSy4ha8nPww1prTZGxiPi8BHDyUXdkJzRux4QQNwZjKay5bJp8Tnylzcwj8xeDeY1Gw4yOMwD49uS3FBuLy7WvzXp5uDQyH5dZQGGJsa69F0IIIYRociwO5o8cOULr1q1rbNe6dWuOHDli6WXEFQpLjByNzeJgdEa57TqtzpwZ+nzWeXD0VXdU8YFaCCHq1d4vIPk4WNmoP2cnQCX5VK6cZg8wOng0nnaepBaksjVua7n2NdWYL+Nqb42TjTrZ7EKajM4LIYQQovmzOJg3mUy1ylB//vz5KhPkibpbczSBcR9v4621pyrsa9lCXTd/PvO8jMwLIa6d3GTY+H/q9yNeVb8aiyA/vVwzk2IiOjsagBCnS8nx9Do9fXzUkpoRmeVzrNQ2mNdoNOYkeJGpuZa9DyGEEEKIJsTiYD48PJw9e/bw888/V9nml19+Yffu3YSHh1t6GXGFNl6OAJxJyqnwkCTUWU18F5EVAY4Xg/kcCeaFEA3sr3lQlA0+XaDnTLD3VLdnx5VrlpCXQJGxCL1Wj6+Db7l9gY6BwKWR+zIxtZxmD1wWzMvIvBBCCCGaP4uD+Tlz5qAoChMnTuS+++5jzZo1nDx5kpMnT7J27VomT57MxIkT0Wg0zJlTsdyQsEwrTwc0GsjILyE1t/za0rIkeOrI/MUPytkyzV4I0YBi9sChZer3t7wLWt2lmUFXPEwsK0sX6BiITqsrty/IWU2SWjZyD2A0KcRmFKjH1CKYD5aReSGEEELcQGqdzf5KY8eO5d133+WZZ55hxYoVrFixotx+RVHQ6XT85z//YezYsVfdUaGy0esIcrUjKi2fM0k5eDgazPvKptlHZEVgCrpTfVIjI/NCiIaiKLD2WfX7rpMhoKf6vZMfJByuMDJf2Xr5MkGOajB/IfuCeVtCVgGlJgVrnRYvJ5sau2OuNS8j80IIIYS4AVg8Mg/w5JNPcuDAAe6//35CQ0MxGAwYDAZatmzJ/fffz759+3j66afrq6/iosun2l8uwDEAK60VBaUFJFiXJaGSkXkhRAPJT4P4A+r3w+de2l62zOeKvz9XlqW7XJCTGsxnFGWY682XrZf3d7FFp9XU2J2yYD5Cas0LIYQQ4gZg8ch8mU6dOrFo0aL66IuopTBvR/48kVQhmLfSWhHsFMy5zHOcVwrxA8hJVEfPNDV/EBZCiDpJPat+dQ4AB89L283LfK6YZl/NyLyd3g4PWw9SClKIzo6mo3tHYtPVKfY1Jb8rUzbNPjW3iJzCEhxt9LV/L0IIIYQQTcxVjcyLxtH64sj86cScCvtCW1xMglecqW6oJKO0EELUi7SLwbxbq/LbzcH8FdPsL66Zr2xkHi6NzpcF/Zcy2dvWqjtONnrcHazVc8hUeyGEEEI0cxLMN0FhF4P5s0m5FTPaXwzmz2VHgZ27ulFqzQshGkLZyLx76/Lby4L5y3J25Jfkk5SfBECIcwiVKQvmy5LgRdchk30Zc0b7NJlqL4QQQojmzeJp9kOHDq11W41Gw4YNGyy9lLhCiLs9VloNOUWlJGQV4tvi0qhVufJ0Tj6Qn6pOdfXu1FjdFUI0V2nn1K9uVwTzjhWraZQltnMxuOBscK70dFWNzNclmA92s2dvVAaRKRLMCyGEEKJ5sziY37RpU41tNBoNiqKgkfXa9craSkuIuz1nk3M5k5RTPpi/ODJ/PvM8ikMgGo7KyLwQomGYR+avnGZ/MQFeUTYU5YDBsdr18mUCndRa82Uj8zHmafZ1GJn3KEuCJ+XphBBCCNG8WRzMb9y4sdLtJpOJCxcu8Pvvv7Nq1Sqef/55Ro4caXEHReXaeDuag/nBYZcSTwU6BmKlsSK/NJ8kZ1e8oUISKiGEuGrGUshQs9NXGJk3OILBSQ3msxPAw7HG9fKX77uQfYGcwhLS8oqBugXzrTwcADiXLMG8EEIIIZo3i4P5QYMGVbt/+vTpfPjhhzzzzDNMnDjR0suIKrTxdGQ1CZxJKv+BVa/TE+gUSERWBOcNNmowLyPzQoj6lnkBTKVgZavWlb+Sky+kZKt/fzzaEJl9sSxdNSPz/o7+aNCQW5LLsUT171YLOz1OdchKX5Yg9HxKLiaTgrYWJe2EEEIIIZqiBk2A99hjjxEQEMArr7zSkJe5IYV5q6NPV5ang8uS4GlN6gYZmRdC1LfUyzLZayv5X8kVteZrMzJv0BnwsVePO5BwRm3vZl+nbgW42GKt01JYYiIus6BOxwohhBBCNCUNns2+S5cubNu2raEvc8NpfVlGe5Op8oz2EaaLH2SzZWReCFHPzGXpQivfXzZanx2HoijmBHjVjczDpSR4p1IjgEvZ6WvLSqel5cV182eTKz7sFEIIIYRoLho8mE9PTyc3V9Yu1rcgVzusrbQUlBiJzSg/+lSW0f588cX68jLNXghR36oqS1emLAledgLJ+cnkl+aj0+gIcAio9rRlSfDKgv+6BvMArTxl3bwQQgghmr8GDea3bNnC1q1bCQ2tYuRGWMxKpyX0YqKn01dMtS+r4RyRd3F6fUEGlMh0UyFEPaqqLF0Zp0vl6coCc39Hf/S66te/l03DTymMU3++imD+bJIE80IIIYRovixOgPfqq69WuS8nJ4eTJ0/yxx9/YDKZmDlzpqWXEdUI83LgZEI2Z5JyGNHey7w9wFEd+copySVLb4tzSQHkJIBry8bqqhCiuSkL5q8sS1emrNZ8TvylsnTVrJcvUzYyn2dSH0aG1HHNPEBrT3UZ0rkUCeaFEEII0XxZHMy/8sor5jryVdFqtTz++OM88cQTll5GVKONt/qB9cokeHZ6O1xtXEkvTCfO2Qvn1Cg1CZ4E80KI+lCYDblJ6ve1GJmPzLqYyb4WwXxZG5NVKmAi2L32ZenKmKfZJ+WiKAoajWS0F0IIIUTzY3Ew//LLL1f5Acna2ho/Pz+GDh2Kv7+/xZ0T1WvjWRbMVxx98nfwV4N5exfap0apI/NCCFEfypLfOXiBjVPlbcoS4OWlEJVVc1m6Mr4Ovug0OozaEtycC3GsQ1m6MsHudui0GnKKSknKLsLb2abO5xBCCCGEuN5d1ci8aFxhF0fmzyfnUmo0YaW7lALBz8GPI6lHiLO5OKolGe2FEPUltYb18gB2rqAzgLGIqMzzwKVM9dWx0lrhYu1NalEcXm6WZaM3WOkIcrUjIjWPc8m5EswLIYQQolmyOAFedHQ06enpNbbLyMggOjra0suIavi1sMVWr6PYaCIqLb/cPn9HdUZErJVO3SAj80KI+lJTWToAjQacfCgB4vPVKfm1mWYPYIs3AE6OmRZ30ZwET8rTCSGEEKKZsjiYDwkJYc6cOTW2e+aZZ2jZUtZqNwStVkMbr7KszeU/sPo5qFNcYzGqG2RkXghRX8zJ76oZmQdw9CXeygoTCrZWtrjbutfu/KVqOytDmsVdlPJ0QgghhGjuLA7mFUWpNvndlW1Fw2jjpU61v7I8nZ+jGszHmS6WpJOReSFEfanNNHsAJ1+i9epqrgDHgFonoivIcwWgWJNkcRdblz3olGBeCCGEEM1Ug9aZB7VMnbW1dUNf5oZVFsxfWU/Z30GdZh9XlIkJ1Gz2QghxtUym2o/MO/mYg/lAx8BaXyItU02ql2W0/O9WK49LOUWEEEIIIZqjBgvmTSYTR48e5e+//yYwsPYf4kTdlJWnu3Jk3tveG51GR4lSSopOp47Mm0yN0UUhRHOSHQelBaDVQ4saEto5+RFjpWajD3AKqNXpM/KKyclxASC5IJ5SU6lF3Qz1VOvTp+UVk55XbNE5hBBCCCGuZ3UK5nU6nfkFsHTp0nLbLn/p9Xq6du1KWloaEyZMaJDOC8xr5qNS8ygqNZq3W2mt8LZXk0jFWVmBqQTyLV9/KoQQwKXkd64hoKuhIIpj3UfmI1LzUEqdQNFTaiolIdey0Xk7ayv8WtgCsm5eCCGEEM1TnYL5snXyiqKg0WjK/Xzly8rKiuDgYJ566inmzZvXUP2/4Xk72eBoY0WpSSEyNa/cvrKp9rEO6igXOZIETwhxlWq7Xh4ujszXLZiPSs0DtBgUTwAu5FywpJfA5evmJaO9EEIIIZqfOgXzJpPJ/FIUhenTp5fbdvmrqKiI8+fPM3/+fIvXzC9cuJDg4GBsbGzo3bs3e/bsqbLtV199hUajKfeysWn+tYU1Gs2lJHiJVSTBs1XXn8q6+RtTRl4xS7ZH8sjyAzz87f5yr3f+OM2ZJAl0RB2Y18u3qrFpqYMnsWUJ8C5W2KhJVJr6UNLF2heAC9lXEcyXladLkpF5IYQQQjQ/NcyRrNrcuXMJDw+vz76U8/333zN79mw+++wzevfuzfvvv8+oUaM4ffo0np6elR7j5OTE6dOnzT/XNnNyU9fa04H9FzI4n1J+ZN5cns7aoG6QkfkbhtGksOVsCiv3xbD+RBIlxqorSny88RxtvR0Z39WXcZ19CXC1u4Y9FU2OucZ8zcF8olahVKPB2qTgpdTu2XHExRlGfvYBJGbtvapgvqw83fkUCeaFEEII0fxcVTDfkBYsWMADDzzAjBkzAPjss89YvXo1ixcv5rnnnqv0GI1Gg7e3d4P263oU6lH5B1bzNHvdxYcaMjLfbBWWGDkal8XB6AwOXMhk34V0UnMvJf3q6OfEmE6+OBh05m2lJoUd59PYdDqZU4k5nFp3mvnrTtPSw56+Ld3oG+pGn5ZuuDsYGuMtietVHabZx+Spf3P8S0vR5iaCk0+Nx0RdDOZbuYSwP+vqRuZbeVZe7UMIIYQQojmwOJhvSMXFxezfv5/nn3/evE2r1TJ8+HB27txZ5XG5ubkEBQVhMpno1q0bb7zxBh06dKi0bVFREUVFReafs7Oz6+8NXGPm0acrkjyZp9krF4M6GZlvdpKzC3lt9UnWHUuoMPruYqfntnA/7uoeQHtfp0qPn9E/hKz8EtYdT+B/h+LZGZFGREoeESl5fLs7GlCTLKrBvTt9WrrSwk5KTd6wSgogK0b9vqaydEBMjto2sKQEsuPBt/rZXIqimIP5zl6t+D7qaoN59W9jYnYhOYUlONroLT6XEEIIIcT1ptbB/NChQ9FoNCxduhR/f3+GDh1a64toNBo2bNhQ6/apqakYjUa8vLzKbffy8uLUqVOVHhMWFsbixYvp3LkzWVlZvPPOO/Tr14/jx4/j7+9fof2bb77ZbBLzlY3MR6TmYTQp6LTqSHzZNPtkYz7FgLWMzDcbJpPCir0xvLn2JDmFaukudwcD3QJb0C3IhfCAFnQNbIHBSlfDmcDZTs/dPQO5u2cgWfkl7I5MY8f5NHZFpHEqMYczSbmcScpl6c4LaDTQztuJR4a0YkznmkdZRTOTdh5QwKYF2LnV2Dw6W30gFFBaqgbzNUjJKSKv2IhWA738w2A3xOfGU1haiI1V3XOgONvq8XQ0kJxTxLnkXMIDXep8DiGEEEKI61Wtg/lNmzah0WjIz883/1xb12Ltet++fenbt6/55379+tGuXTv++9//8tprr1Vo//zzzzN79mzzz9nZ2QQE1K4O8vXGz8UWaystxaUm4jIKCHRT1zy72bhha2VLQWkBCVZWBOVIMN8cnEvO5YVVR9kTlQ5AF39nXr+tEx39nK7635qznZ6RHbwZ2UFdrpKWW8TuyHR2nk9jZ0Qa55JzOZGQzSPLD7DlTABzx7fHzvq6nOAjGkLZenn31lCL/9aic9RgPrCkdsF8WUUOPxdbvOzdcbJ2Irs4m+icaNq4tLGoy608HSSYF0IIIUSzVOtP4Rs3bgQgMDCw3M8Nwd3dHZ1OR1JSUrntSUlJtV4Tr9frCQ8P59y5c5XuNxgMGAzNYy2wTquhpbs9pxJzOJ+Saw7mNRoNfg5+nMs8R6zeiqBafJgW16eCYiMbTiXx66F4Np5OpsSoYGet4+mRYUzrF2yejVHf3BwM3NLJh1s6qaPwKTlFfLUjkk82nef7fTHsu5DOx5O60c6n8mn8oplJq0NZOi6bZl/LkfmyTPYh7g5oNBpCnEM4nHKYyKxIi4P51p4O7DifJrXmhRBCCNHs1DqYHzRoULU/1ydra2u6d+/Ohg0buO222wC1LN6GDRt49NFHa3UOo9HI0aNHueWWWxqsn9eTUE8HTiXmcC45lyFtL2X7Lwvm46ysICdTXfOqt228joo62RuVzvLd0fx5PJG8YqN5+5AwD167rSP+Ltc287yHo4E5o9rSP9SdJ74/xPmUPG5duJ3nR7dlSp8grHR1qnYpmprEY+pXj7Aam5oUkzmYDygpqVXOjrJM9iEXH0heHsxbqmzd/FkJ5oUQQgjRzFy382Nnz57NtGnT6NGjB7169eL9998nLy/PnN1+6tSp+Pn58eabbwLw6quv0qdPH1q1akVmZiZvv/02Fy5cYObMmY35Nq6ZqjLaXypPZwPkqqNjbqHXunuijnKLSnljzUmWX0xCB+DXwpbxXX0Z38W30UfC+7VyZ+3jN/H0ysNsPJ3CvN9O8PXOCzw+rDXjuvg22EwB0chi96lf/XvU2DQ5P5kiYxFWGi0+pcbajcxfDOaD3e0BNZgHrjKYVzPay8i8EEIIIZqb6zaYv/vuu0lJSeHll18mMTGRrl27sm7dOnNSvOjoaLTaS6OAGRkZPPDAAyQmJuLi4kL37t3ZsWMH7du3b6y3cE2FeqgffiuUp3O8WJ7O1h5IhZwECeavc9vOpvLsT0eIyywA4M7u/tzbK5BugS2uSf6J2nJzMLB4ek++3nmB9/86Q2RqHk98f4iP/j7LE8PbMKaTD1oJ6puP7HjIjgWNtsas9HBpir2fnTdWRKmlMRWl2rX2UalqTpaQi8F8sFMwcLXBvPqgMyYjn8ISIzb6mpNCCiGEEEI0BbUO5l999VWLL6LRaPj3v/9d5+MeffTRKqfVX5mA77333uO9996zpHvNgrk8XUpeue1lI/NxVhdLMklG++tW3sXR+LKScP4utsy/szP9Qt0buWdV02g0TOsXzB3d/Vm6I4rPt0RwPiWPf313kI//PseTI1ozsr23BPXNQdmovGcHsLavsbk5k71TELALSvKgMAtsW1Ta3mRSLlszX35kPio7CpNiQqup+zIOdwdrWtjpycwv4WxSLp38net8DiGEEEKI61Gtg/lXXnkFjUaDoig1N76orL2lwbyovZbuajCfnldMel4xrvZqLXBzMK8xqQ2z4xqlf6J6+y+k8+T3h4lOV0cmp/QJ4rnRbbE3XLeTZ8pxMFjxyJBWTOkbxJJtUXyxLYLTSTnMWnaA9j5OPDmiDcPbeV5XMwuuV4qiEJ2ez4HoDA5FZ1JsVPBwNODhaMDT0YC/iy3tfa6+ckGdxe5Vv9Ziij1clsneOUQtZVeYqf79qSKYT8gupKjUhJVWg18LNa+Hv6M/VhorCkoLSM5Pxtu+dglQL6fRaOjo68y2c6kcjs2UYF4IIYQQzUatI4W5c+c2ZD/EVbK11uHXwpa4zALOp+Tiau8KXJpmn0UpORoNjvEHGrOb4golRhMf/HWWTzadw6So6+LfvrMz/Vpdv6Px1XGy0fP48NZM7xfMl9siWLw9ihMJ2Tzw9T46+Dpxe7gfozp4E+B6bRP3Xa8Kio2cT8nlfEou55JzOZmQw6GYDFJzi6s97vZwP96+s/O1TThoXi/fs1bNzZnsnQLBoy3E7IK4A+DVodL2ZevlA93szO9Lr9UT4BRAZFYkEVkRFgXzAJ391WD+SGwmEGTROYQQQgghrjcSzDcjrTwd1GA+OZeewWowb6+3x8XgQkZRBnF6K9pe2FHjulVxbZxLzuHJ7w9zNC4LgAnhfrxyawecbPSN3LOr52ynZ/bIMGb0D2HR1gi+2hHF8fhsjsdn8/rqk7T3cWJUB286+zvjZKvH2dYKJxs9znZ6DFbNe01zXGYBP+2P5X+H4iosiymj12no4OtMt0AXnGytSM4pIiWniOScIo7HZfHzwTiKSo18cE84+msR0BtLIP6g+n0tg3nzNHvHAAjqpwbzF3ZAtymVtr+Uyb78FP4QpxAisyKJzIqkn28/i7rfJaAFAEdisyw6XgghhBDietQ05vCKWgn1cGDzmZQKWZv9HPzUYN7alra5KWqtaPfa1YkW9Ssjr5i1xxL536E49kSloyjgbKvnjds7MaazT2N3r9652FvzzM1t+ceAEH4/ksC6Y4nsiUrnREI2JxKyK7S30mq4uaM3M29qSdeLAVhTpygK6XnFbD+fxsp9MWw7l8rlq5Vc7PS08nQg1MOBVp4OhAe2oIOvc5WJ2v48nsgjyw+w5mgixaUHWHhfeMM/AEk+AaUFYOMMbq1qbK4oinmavRrM94dtC+DC9iqPuTKTfZkQ5xCIubokeF38WwBwJimH/OJS7Kzlf31CCCGEaPrq7RONoiikpaWhKApubm7lMs2LayPUs+qM9sfSjhHrFgy5R9QP1BLMX1P7L6TzycbzbD6TQqnpUiQ3rK0n/3d7J7ydbRqxdw3PzcHAtH7BTOsXTHpeMX+dTOKvE0nEZxWQXVBKVkEJOYUllJoUfj+SwO9HEugR5MLMm0IY0d670lJ3JpNCam4RMRn5GE3Q1sex0Wc1pOcVszsijb1RGVxIyyMmI5/YjALyi43l2vVp6crEHgEMauOBm4OhTtcY2cGbz6f2YNY3+/nrZBIPfr2f/07p3rBZ2svWy/v1gFr8bU8rTKOgtACtRqvm7bBxV7PgZ16ArFhw9q9wzIl49eFOpcE8ahI8S3k72+DpaFBnNsRnm2cuCSGEEEI0ZVcdzK9fv553332Xbdu2UVCgltKysbHhpptu4sknn2TUqFFX3UlRO608qs9oH+vkoW6I2g7dp1/Lrt2wiktNfLDhDJ9uOk9ZDN/ex4nxXX0Z18XXnOjrRuJqb83EHgFM7BFQbrvJpHAyMZvF26L49XAc+y5ksO9CBnqdBmdbPU42ehxt9djqtSTnFBGbUUBxqancOQJd7Wjv40R7XyecbOr+562o1ERcZgGxGQXEpeXgmXmIVJzIcwxVk9A5qIno7Kx1GKy0GPTq19iMAnZFpHEqMafS82o0at9u7eLLnd0DCHS7upwBQ8I8WTy9JzOX7mPzmRRmLNnLl9N7NNyIcx3Xy5dNsfex98FaZw06a/Dpok7Vv7ATOt9Vrv2+qHR2RqSh1UD/ULdy++qj1jxAZ/8W/HUyicMxmRLMCyGEEKJZuKpPfnPmzGHBggUVMtwXFBTw559/sn79ep544gnefffdq+qkqJ3QKuop+zlezGivvzhqeWG7rJu/Bs4l5/DE94c4FqeOOE4I9+Ofg0Np7eXYyD27Pmm16jrxdyd24dmbw/hm1wWW7bpARn4JqbnFlSaF02rAx9kWRVGIzyokOj2f6PR81h1PrPF6ekrRYqII63LbW2riuUO3hf/TbcNHl06+YmBkxn/Yn+5Zq/fRxsuBPi3dCPN2JMDFjgBXO3xb2NT7VPj+rdxZen8vZizZw86INKYt3sPi6T1xbIjZCeZM9rUM5i+fYl8mqP/FYH5buWBeURTeXHsKgIk9Amh58aFkmWDnYACS85PJK8nDXl9zWbzKdPF35q+TSbJuXgghhBDNhsXB/LJly3j33XextbXl4YcfZtq0aYSEXJwOGRXF0qVL+eSTT3j//fcJDw9n8uTJ9dZpUTk3e2ucbfVkFZQQmZpHOx8nAPwd1CmtscY80Fqp5aEyo8FFsjo3BJNJ4ZtdF3hjzUmKSk20sNPz5u2dGN2p+a2JbyieTjY8NTKMx4a1JiWniOzCEvN0/PziUjwcDQS42OHtbGNOAJeRV8zJBDXJ3qnEHApLjZWeW6eUMDRjJaPTvsGgFFKgtSdb50qOlQvWShGBhafLtbfTFLG65c/s6P0ZKXnFpOYUUVhqoqjESGGJiaJSI442enq3dKVPSzfc6zht/mr0CnHlm5m9mbZ4D3ujMpj85R6+ntELZ7t6DOjz09U8GwB+3Wp1SNnIfKBj4KWNQf1g58dqErzL/Hkiif0XMrDRa3lyRJsK53KydsLNxo20wjSisqLo4F55NvyadDYnwcu06HghhBBCiOuNxcH8Rx99hE6nY926ddx0003l9nXo0IH58+czfvx4Bg8ezMcffyzB/DWg0WgI9bDnQHQm51NyKwTz8XmJKL5d0cTuUz9QSzBf72LS83nmxyPsjEgDYGAbD96+szNeTs17TXxD0eu0+LawxZealyO42FvTr5V79WX9IrfA6qcg9Yx5k60pD1tTHl4laik1NDpoPQK6TlKTvX0+GOe4zYzW7IC+d17tW6p33QJd+O6BPkz+cjeHYzKZ9MUulv2jNy721jUfXBtx+9Wvbq3BrnbT08uVpSsT2Ff9mnoGclPAwYNSo4n/rFNH5f8xIKTKfychziGkFaYRkRVheTDvp9aXj0rLJyu/pH4feAghhBBCNAKLs9QdO3aMAQMGVAjkL1e2/9ixY5ZeRtRRq4tT7S/PaO/t4I1Wo6XIWESqf3d144VtjdG9ZstkUvhmZxSj3t/Czog0bPU6Xr21A0tn9JRA/nqQnQA/PQBLx6nBpL0H3PYZPHsBHt0H01fDnUvgtk/hqVMw6Xtof6taE/2mp9VzrHsOCjIa931UoaOfMyse7IO7gzXH47O55/NdnEnKqbAEyiJ1nGIPVUyzt3MFz4uBeLQ6Ov/9vhgiUvJwsdPz0KDQKs9XH+vmXeytCXRVcxUcicu0+DxCCCGEENcLi0fmbWxs8PX1rbGdr68v1tb1NEIkahRaSRI8vVaPt5038XnxxLq3xAMqTHUVlruQlsdzPx01j8b3CnHl7Ts7E+Rm2dpeUY/yUmH7+7BnEZQWAhroOROGvgS2LdQ2ti2qr+4w4Ak49hOknob1c2H8hw3ebUu09XZixYN9mbRoF6eTchj53hb8XWwZ1MaDwWGe9At1w95gwZ98czDfo1bNFUUhJvviyLxjIIUlRo7FZVFcaqJ3YF90ycchajv5rcbw/l9nAfjX0NbVViKoc0b7KnKCdPZ3Jjo9nyOxWdzU2qN25xJCCCGEuE5ZHMx3796dI0eO1NjuyJEj9OhRuw+B4uqZg/kra807+qnBvIML4WggPUIdrXSSddyWSsst4uON51i26wIlRgVbvY5nbw5jat9gtJWUUhPXUEEG7PgYdn0KJRcfbAX0htH/Ad/wup3LygDjPoAlN8OBpdDlHnX993WolacDK2f15ZVfj7P9XBqxGQV8uzuab3dHY6XV0M7HiW6BLegW5EJ4gAvujtU/aFVMJmxi9qED/hvhwt8HdhKRmofJVH7E395ghb+LLf4utng4GckpUbP6P7k8hlPxZygxqu0n2joyH8g7u4VF1hGk5BQR4GrLfX0CqU6tR+YVBbZ/AFsXgMERvDuBd0f1a0Afuvi34PcjCRyOyaz+PEIIIYQQTYDFwfyLL77IsGHDmD9/Ps8880ylbd5++21OnjzJRx99ZHEHRd2UTbOPSM3FZFLMQaW/gz972Ut0Yar6wTbxiDrVteMdjdndJim/uJQvt0by3y0R5BaVAnBTa3dev62jjMZfD6J3w/KJUJip/uzTVR2JbzXc8goOQX2h2zQ1mP/tcZi1TQ3yr0NBbvYsmdGL/OJSdp5PY9PpFDadSSYmvYCjcVkcjcti6c4LtTpXqCaODYZsChRr5h+0wkh6pe3S8oqJTs8HQGsTjX0ImEqcORqjbitLCrgxtzXYgG36KRb/dRBw4OmRYTVm+i8L5i9kX6DUVIqVtpL/dZUWw+9PwKFv1Z+LsiA7Fs6sVX+2smXAkEWAVjLaCyGEEKJZsDiY12g0PProozz//POsXLmSKVOmmLPZR0ZGsmzZMvbv389jjz2GVqtly5Yt5Y4fOHDg1fVcVMrfxRZrnZbCErVedsDFNaKtXdRpxKfSTqklohKPqPXmJZivtcISI9/vjeHjjedIySkCoKOfE8/d3I4BratJuiaunZTTlwJ5j3ZqEN92TP2UYRwxD06vVdfc//EC3PJO3c5rMoK2fsvTVcfO2oph7bwY1s7LXLrvwIUMDkRncCA6kxPxWeYR86p006rT4CMNYUztEUp7HyfCvB3LBd8KCtkFpcRm5BOTXsCelAscKQZPWz+euKcr3QJd8HexxaTArog0Un4IwKM4hh7aM6T5DWVc55qXa/nY+2DQGSgyFhGfG18+sR5AXhr8MEUtu6nRwag31Lr2iUcvPrjcBWlnabv5Ibpqn+NQdijJ2YV41pDPYuPpZBwNVnQPckEjpTyFEEIIcZ2xOJgfPHgwGo0GRVHYv38/Bw4cKLe/LPHShx9+yIcfll9jqtFoKC0ttfTSohpWOi3B7nacScrlfEquOZjv4KYmnjqRdgI6Pg67P5V187VUXGrih30xLNx4joSsQgACXe14elQYYzv5NNsp9YqikJiXSKlSir+D//UfzGQnwLI71EDerwdM+w2s7erv/LYuMHYBfD8Z9n4BejsY8WrNAX3KGXU0P/EI9HoA+j1W66zw9UWj0eDXwha/FraM66IGzyVGE6U1BPP6NX/AIWjfcyhzR1SfRb5XiPqe5u1czpEzMCy0E7d29TPv12mgfyt36DgEDnzN612zsBvbs1b/frQaLUFOQZzJOENUdlT5YD75FHx3N2REgcEJ7lqizsIAdUYFQEkhLL8LTeQWvjbMZ0Lhvzkcm8WI9lUH838cT+Shb9RM/t2DXHhkSChDwjyv/38HQgghhLhhWBzMDxw4UD7UXKdaeTpcDObzGBymbmvr2hYNGpILkkn1aos7QMpJdUTL3q0xu3vdKio18tP+OBZuPEdcZgEAPs42PDq0FXd1D8DayuJiENclk2LibMZZDiQf4GDSQQ4kHyApPwkAR2tHOrh1oKN7R9q5tkOv1VNsKqbYWEyRsQiTYsKgM2Cts8ZaZ42NzoaO7h1xNjhfm84XZsG3d0JWjFpObtIP9RvIl2k3Dsa+B78/CTs+VKfaD32p8rbGUtjxAWz6DxjVmRxsew/2fgl9Hoa+D4PNNfr9VEKv06KvaaJAwsWydLXMZF9qKuXv6L8BGBowtPJGQf3hwNf4Zh0Au9onRw1xDuFMxhkisyIZ6NVLnT5/aDmc+wsUE7gEw73fg2fbigfrbeCe5fD1rTjF7WeZ9Zv8dq4NtPeq9FpFpUb+b/VJbCmkEGv2X8jg/q/20c7HiYcHhzKmGT/EE0IIIUTTYXEwv2nTpnrshqhPZUnwLi9PZ6e3I8Q5hIisCE4UJDLQoy2knILondBubGN19bqUV1TKd3uiWbQ1gqRsNQjzdDTw6NBW3N0zoMb1vU1JbE4suxJ2sSthF7sTdpNZlFluv5XGCo1GQ05xjrldbTnoHbiv3X1MaT+lYYP60iJYcR8kHQN7T5j8U8M+oOpxPxhLYO0zsOVt0Blg0JzybRKPwv8egYTDlALFocPQd5qIfufHkHQUNr8Fuz+DzhPV+uuBfcCp5unm11RhFiSfUL/3q10S0/1J+0kvTMfZ4ExPnyoeAAT1V7/GH4KiXDA41OrcIU5BAEQe/Q5++7favzKthsPtn1d/3w2OcN+PZC4chndeBOOPPAxDNoFjxYB+yfYoOmX+zds2n0PwAD7wfJ1luy5wMiGbf313kG93X2D+HV0IdGuAB0ZCCCGEELVkcTAvrl+XytOVz2jf3q09EVkRHE87zsCgfmowf2G7BPMXRaXm8cuhOL7aEUVmfgkA3k42PDiwJZN6B2JT4zBm07Etbhvv7nuXc5nnym23tbKlq0dXwr3C6e7ZnY7uHdFr9ZzNPMux1GOcSDvB2Qx1HbVep1dH47XWaDQa80h9sbGY1IJU4nLj+O+R//LtyW/rL6jPilNHt3MSoDgXivMgNwkyo8HaASb/qI7QNrTeD4GxGP58CTa+Tkn6eU6W5nAgJ5IDpZkc0ynkWmspCg7ApNGA6SzWR99mWPuh3NblVvoc+AFt6hnY87n6AmgRpAa64fepXxt75tOOj9QRb/ewWle9WH9hPaCOyuu1VZSaaxEAzoGQFQ2xeyB0qBrUx+5Vp8rbu4ODFzh4gp0bxO2HE78SErEanK2JzDqvBvJO/mplga6TwK3qGvXl2LkSN+47cpaPJaA0HuW/A9GMXaDmVbgoJbuQkr/fYqH1D+qGmK08P7UN/xwcylc7ovjv5gh2RaRz8wdbeG50Wyb3DpJReiGEEEI0Co1Strj9BpednY2zszNZWVk4OTk1dneuyrG4LMZ+tA1nWz37XxqOlU6dDv7NiW+Yv3c+gwMG85HHIPjpH2qSqIe21HDG5qmwxMjOiDQ2n05h0+lkotLyzfuC3ez45+BQbg/3b1bT6WOyY5i/bz6bYjYB6sh7Z4/O9PHpQx/fPubg/WqZFBMbojfw6eFPzcG/vd6ecS3HcXfY3bRyaVX3kxblwJcjL40WX05nDZO+VwPDa8BoMnIq/RQ7d8xnd/x2DhkMFGpr/9+Jj70Ptzq34+4icI89oM4qUEyXGni0U9fXd7671iPX9SorFj7qDqWFcPcydXlBDYwmI8NWDiOtMI1Ph3/KAL8BVTde9RAcWQG+3dT3nXgUFGO15z9prWeinw8uGiu2DHgPggdCHX7nZYpKjdwydymf6+YTqk1QN3a8A0bPB2sHDn48mfCs9eUPeuwQuF7MqJ+Wx5wfj7AnUs3s36elKw8NDKWgxEh2QQnZhSVk5peQmltEck4RKRdfTrZ67usdyF09AnAwyHN0IYQQQlSttrHpVQfzhYWF7Nu3j/j4eAoLC6tsN3Xq1Ku5TINrTsF8qdFEz//7i4z8EpbP7E2/Vmqm9f1J+5m+bjqedp5sGPUNLGgHGi3MPlXpVNPm6lxyDst2RfPT/lhyii4lYrTSaugR7MJ9vYO4pZMPumY02pZfks8XR79g6fGlFJuKsdJYMandJB7s/GCDToGvLKgH6OHVg7vD7mZwwGBsrKrPKK6eyATf3wen16ijtoOeUZOd6e3A2h482tZ69NhSJcYSNsVuYm3kWnYn7Ca7OLvcfmetgXCnELp5dCU8cDDuzkFY66wx6AzotXoisyP55ewvrI5cTU6xWofd086Tr0d/jZ+VI8TtgxO/wpHvoeTigyWDEwycA/0fa9D3VsGqB9V+BPWH6atrNUtgb+Je7v/jfhytHdk8cTN6XTUPhfYvhd+ueE/Ogep694IMyE1WZ1yUFoKtK7QbS37YzfTe9RwAW+7egouNi8Vvb/zH2zgdm8LqTltpdW6J+kDBzo0COx9sU49RouiI6/8awee+heTjMGkltBlpPt5kUvh6ZxT/WXeagpLqH0JcydHGint7BTKtXzB+LWwrbWM0KRyLy+JwbCa9Qlxp6920/58khBBCiLq5JsH822+/zRtvvEF2dnaNbY3Gun3gudaaUzAP8NxPR1ixN4bJfQJ5/bZOgBrQ9VneBwWFjRM34v7tPerU1qEvqQFDLUSm5jHrm/0Y9Fra+zjRwdeJ9r7OhHk7VjralJ5XzO6INHZGpBGZmkfXgBYMDvOga4BLvQXL55JzOZecg4NBj5OtFU42epxs9WiAolITRaVGikpNnEnK4dtd0eyMSDMf6+Nsw+AwDwa18aR/Kzccba5+VPp6kl+Szw+nf2DJ8SWkF14cSfTpw/O9nqdli5bXrB8mxcTuhN18f/p7NsZsxHRxFFqDBh97HwKdAglyCiK0RSijg0fTwqZF+RNseBW2vquuT5+xBvxrt4a7PpzJOMPPZ39mdcRqMooyzNsd9A709O5JH58+9PbpTYhzCFpNzSPFRcYi/o7+m08OfUJUdhQBjgEsvXkpHnYeaoOCTDj8HexZBOnn1W33/wmBvRvg3VUibj8sujjD4cFN4Bteq8Pe2P0G3536jltDb+X1Aa9X37g4H9Y9C1Y2ar6AgD7g7Fe+jaKoSyn0duaSfiN/HElCXgJfj/6acM/a9asyL/1ylGW7ohnXxZd/dyvEY8NsNBdnfGQq9nwd+DqP/eN++GEanPhFLXXX95EK57mQlsf/rT5JVFoezrZ6nG315r8/Ho4GPBwMeDipXw/FZLJ4WyQRqXkA6LQaQj3sCXCxw9/F1lx5ZFdEGrsj0s0PGsO8HPnjSSnlKoQQQtxIGjyY//jjj3nsMXVkpVOnTrRu3RpHR8cq2y9ZssSSy1wzzS2Y33wmhWmL9+DuYGD3C8PMgfOtv9xKRFYEC4ctZGBaPPz8EDgHwOOHa6yBbTIp3LNol3l66ZUcDFZ4OhpwdzTg7mBNREoepxJzKm3bwk7PTa096ODrhI2VFhu9DoNei7VOx5UxvkGvxcPBBg9HA24O1lhpNRyNy2LdsUT+OJ7I+ZS8Ov1utBoY1s6LKX2CGNDKvVmud60siPd38OfpHk8zNHBo/VSiKC1Sk5jF7AI0aoZ2Xc3ThxPzEvnp7E+sOrOK5ILkCvvt9fZMajuJqe2nqkH90R/VJSEAt/9XXSd9DcTnxvPithfZl7TPvM3T1pNxoeMYGjiU9m7tsdJaPl06OT+ZaWunEZsbS6sWrVgyakn5hxgmk5pE7/By8OsO//jLomnldaIosPhm9Z52uRdu/6xWh5kUE8NXDielIEX92+LfMMHnQ+sfYkf8Dl7p+wp3tLnD4vP8tD+Wp1YeNv/s56jlcdu1uGQc4x3lPhY/PUkdNf/7/2DLfOg+HcZ9cNX9N5kUNp5O5outkeUeKlbG0WBFTlEpGg0cnzcKO2uZmi+EEELcKGobm1r86eDjjz/GysqKn376iXHjal5PKa6tfqFuONvqSc0tYm9UOn1aqlmeyyXBaz8D1j2vlvM6+yeEja72nCv3x7AnMh1bvY5Xb+1AVFoex+OzOR6fTUpOEblFpeQWlZpHnsq08XKgb0s3Qj0d2BOZzpYzKWTml/Db4Xh+Oxxfp/el0YCdXkde8aWZHnqdhnY+ThSVmMguLCG7oMS8X6/TYLDSYbDS4mSrZ2xnH+7tFYhvFdNbm7r43HhWnlnJqrOrygXxD3Z+kLGhYy1fD28yQUakWis94TBE71ZHcMtKroE6y+POxVDd9GrA296bR7o+wsNdHia9MJ3onGiisqK4kH2BbXHbOJ1xmkVHF7H81HIm+Q1l6vbFtAC1Pvs1CuS3xG7h+a3Pk12cjZXWiiEBQ7it1W308+13VQH85TztPFk0chHT1k7jXOY5Zv01iy9GfoGD9cU18lotDJ8LJ/6n/q6Pr4JOd9bLtUk7r446O/upv9egfuo/rhP/UwN5K1sY+u9an+5Q8iFSClJw1DvSx6dP/fSxEq1atGJH/A5OpJ3gDiwP5sd09uF0Ug47zqdyMiGHuBwTz+SMAkbx2NBWl6a/u7dWv6aerfJcdaHVahjWzoth7byISc8nMjWPmIx8YjMKiEnPp6jURI8gF/qGutHB15neb/xFam4xZ5Ny6RLQol76IIQQQojmw+JPpVFRUQwcOFAC+euUXqdlZHsvVu6PZc3RhHLB/O8Rv3Mi7YRaezn8PjVr9d4vqg3mU3OLeGPNKQCeHNGau3oElNufXVhiTvRUlvTJy8lAn5ZuuDsYzO2m9g2m1GjiUEwmm8+kEJdZoE6FL7k0HZ4r5orkl5SSklNEam4xRpNCXrERO2sdg8M8GNXBmyFtPXG6Ynp8qdGERqNpVuveq2I0Gdkev50fTv/AltgtKBd/gRYH8cX5kHxSLaGWWPY6BiWVzICwc1envJ//G07+Ciunw51LwKrm+uEawM3WDTdbN/OU6ce6PcbGmI18euhTNaiP+o0lvu701jowIqgzQwrTcbVxrf17qaNSUymfHPqERUcXAdDRrSPvDH4HPwe/Go60jL+jP5+P/Jzp66ZzPO04j/79KB8M+eBSHgNHb7jpSfj7dfjrFTXruv4qH0SVFqn3Kemo+jqzTq0j3+8xWP+y2qb/4xWnvVejLIv94IDBWOtqXzu+rsI9w/n6xNccSD5wVeex0et44ZZ2AOQXl3IkNosD0RkUlph4ePBlmfHNwfyZq7peZQJc7cxT66sS5u1I6rk0TifmSDAvhBBCiAosDuY9PT3x8PCoz76IenZLJx9W7o9l7bFEXhnXAa1WQ3u39gBqMA9qzewdH8G5DZAeAa6Vr6N+7fcTZBWU0N7Hifv7h1TY72SjrhUtK4tXHSudlh7BrvQIrltQZjIpZOQXk55XTICrXbWl4soy+Dd3qQWpPLHxCQ6nXJoy3NunN/eE3cOggEE1B/GKoo7SRu9UR2Rj9kLa2fKZ1ctY2YBne/DuCP691ProbqHqiO7Z9Wqt91O/ww9TYOLXYHXxIY7JBAmHIHaf+t9Y2SvzAri1hn7/UkecdXq0Gi3DvPsyRLuGjUnb+G8LZ04arNmu5LF916u8uvt1enj1YIDfAPr49CHMNaxW69RLjCXkleRVXItv/jUonMk4w/y989mTuAeAe8LuYU7POQ0anAKEtgjlsxGfMfOPmexP2s/on0Yzo+MM7mt3H3Z6O+j7KOz7Sp1Bs3MhDHz66i7457/VGRa2rmqW+sMr1FkVP0xR9zt41ynhnkkxmYP5kcEja2h9dcoe+pzLPEdWUVa9JG+0s7aiT0s38wPPctwuBvN5KWpiPlvLk+5ZIszLie3n0qpcriSEEEKIG5vFwfzo0aP5448/MJlMaBt6HaewSP9W7jjaWJGSU8T+6Ax6BrvSzrUdGjQk5yeTWpCKu2tLCB0G5zfAviUw8rUK59l8JoX/HYpHq4G37ujUaIGyVqvBzcGA22Uj/TeycxnnePTvR4nLjcNeb8/trW5nYthEQpwrPmypoKQA1j4Lp1ZDfmrF/Xbu4NMZvDuBd2fw6ghurapeE996BNz7HayYpI70rpgEne6Cc3+po/b5VawPTj4Ov8xSR577PQo+XeG3x9CmnmEYMKzTNCJ6Tuev+K38deEvTqafZE/iHnPA3cLQgt4+vens3hkPOw/cbNSRfkdrR85lnONA8gEOJB/gaMpRCo2F+Dn40c2zG928utHFowvxufFsid3C5tjNJOUnAWBrZcsrfV/hlpa31Px7rCcd3Drw+YjPeWXnK5zJOMOHBz/k25Pf8lCXhxjgN4DE3tNJ3LmA+MOfkkI6BVoNJcYSioxFFJuKcbJ2ItwznHDPcFq1aIXuivwXRpORvNI8ik+upvjAFxTprSge+TKZ3u1Ja9WX9DOrSYvdTa5Sgn2rwTic/g5HvSMO1g60c21XbbLEIylHSMpPwl5vT1/fvg36e3KzdSPYKZio7CgOJh9kcMDgBr0eBgdw9IWceEg9BwE9G/Z6V2jrreahOZVYc5JZIYQQQtx4LE6AFx8fT48ePZgwYQILFizA2rphR68aWnNLgFdm9g+HWHUgjun9gnllfAcAxv8ynsisyEuJqk6tVoMvW1eYfVKdfn9RQbGRke9vJia9gBn9g5k7rkNjvRVxmR3xO3hq01PkluQS6BjIwmELCXYOrt3BxhJ1FP3sH+rPOoOaYC2wt5pV3LerWv7NkiR5EZvhu3sulVYrY+0Iwf3BvY06+8M1BJz84ORvsOtTyLsiEZ6DN9z2CbQaVm5zTE4Mm2I2sTthN3sT95JfesV1roKNzoY+vn14stuT1zTT/+VMiom1kWv5+ODHxObGWnQOR70jHd07YsJEemE6aQVpZBZlmisIWKKTeydub307NwffjKN1+USnb+99m69PfM2YlmN466a3LL5Gbb2y4xV+OvsTMzrMYHaP2Q1+PZaOh8jNcOsn6rKka+hIbCbjP96Om701+/894ppeWwghhBCNp8ET4Pn6+rJt2zbGjx9PWFgYQ4YMITAwsNJReo1Gw7//XftkSqL+3NLRh1UH4lh3LJGXx7Y3T7WPzIrkRNoJNZhvPQqc/CE7Vk2A1eVu8/EfbDhLTHoBPs42PDUyrBHfiSjz45kfeX3X6xgVI908u/H+kPdrX3PbZIJf/qkG8lY2asK6VsMvTYm/Wi0HwX0/wqoHwM5NHbEPHQYBvSpPjHfTbDUL/uHlsP0DyIiCduPVzOF2FZdhBDgGMKX9FKa0n0KJqYRjqcfYGb+Tc5nnSC9MNwev2cXZ5lH4cK9wunl2w9POkyMpRziQfICDyQc5mnIUVxtXbvK/iUH+g+jp3bN2Ne8bkFajZUzLMYwMGsmqs6v4/OjnZBZm4uPgg7fODp/ovXgaTdj1mInBJQRrnTV6rZ6k/CQOJB3gcMphckpy2Jmws9LzaxQFAxr0BkestdY4G5zVvAU2brjauGKvt6egtICc4hxyS3LJKMzgSMoRjqYe5WjqUebvmc9N/jcBkFaQRnphuvmhw4igaxNshnuG89PZn6563XytubdRg/m0+kmCVxetPR3RaCAtr5iUnCI8HGVWkhBCCCEusTiYVxSFDz74gFOnTmEymfjqq68qtNFoNCiKIsF8I7qpjTsOBisSsws5GJNB9yBXOrh1YHXE6kvr5nVWaumlja+rifAuBvMFxUa+3hkFwLzxHSqtIy+unWJjMfP3zuf7098DMLblWOb1m4e1xgpKCsvNqKiUosDaOXB0JWitYOI30KYB1jgH94fZJ2rfXm+j5m4InwrZcdAisFazAvRavXlq+ZWMJmOFqeYA/f3609+vP4D5b9P1SK/Tc3fbu5kYNhHgUj/L6p5vXaTmJWg5uNxxpaZSzmSc4XjcTmzyM3ArzMMtPx3X5NM4R+1Eb3BC89AWdWZELaUVpPF7xO/8fPZnzmedN6+Pv5yfgx/9fftb+nbrpJtXNwCOpx2nsLSwVg9gEnITSClIobNH57pfsJ4z2teFrbWOYDd7IlPzOJWYjYej5KkRQgghxCUWR2dvv/02H330EVZWVowdO5bWrVvj4FBz8jNxbRmsdAxv58kvh+JZczSR7kGuFZPgAXSbCpvfgtg9kHAEfDqz+UwK+cVG/FrYMqK9VyO9AwEQlxvHU5ue4njacQAe7vows8Imo9nzhZoULScegvpD+1vVpGaO3hVPsvH/1Ic1aNR67Q0RyF8NnRW4BNXPqSoJ5K90vQbyl6vQxzELICcBYnbDNxNgzDvqg5CLrFLP0X7TW7Q/8b/KTzjugzoF8qCuU5/WYRpT20/laOpRdifsxl5vj6utqzlHgb+Df4MnCizj7+CPp60nyQXJHE09Sk/v6texn8k4w/S108kpyWFC6wk82/NZNbFgbTVgRvvaaOvtSGRqHqcTc7iptQTzQgghhLjE4mD+iy++wM7Ojq1btxIeXnFkTFw/Rnfy4ZdD8aw9msCLt7QzJ8FLyk8irSANN1s3cPRSg8DjP8OyCdB7FptiegFwSyfvJhH4NFebYzbzwrYXyC7OxtngzJs9XuCmmEPwQSc1w3aZqK3qa80cCOgNTj5QnKeWmivKUkvMAYx5t/7qlYtry94Npv4Kv/4Ljv4Avz8JKWfUgH7L2+qsCxRAowbtri3Vl0sIBPYBv24WX1qj0dDZo7Nlo9v1SKPR0M2rG+ui1rE/aX+1wXxiXiL//Ouf5JSo2eBXnV3FoeRDvD3obdq4tKndBd0vtkuPVPNNVLZcpAGFeTuy9liiZLQXQgghRAUWB/MxMTEMHjxYAvkmYFAbD+ytdcRnFXI4NpPwQBeCnYPN6+bL1sAy5CWIO6CWDPv7NV5SbAm2Gkb/li817hu4QcXmxPLNiW9Yfmo5AJ1c2vGuPhCf7/9xKcGcS4haEzx4AJxeq9Z6j92rlpmrzLCXoec/rtE7EA1CbwMTPgePNmoVgN2fqq8y7cbB4BfAq33j9bGBlQXzB5MPVtkmqyiLWetnkZyfTEvnljwW/hj/t/v/iMiKYNLqSTzT8xnuanNXzQ8qHX1Bb6f+m8u4AO6t6vndVK8so/1pCeaFEEIIcQWLg3lvb28cHR1rbiganY1ex9B2Xvx2OJ61xxIJD3QxJ8E7nnb8UjDv3gr+tR+O/0zuhrdxyDrDLKvf4fvf1dGpgN5qbfHAPupon4zW1zujycj2+O2sOLWCbXHbUFCLTdzr0Jqnj23HuvhiBnqfLjDgSTVZXNmUcvfWan3wrFi17ruxGKztL74c1LXoHpLEsFnQaGDgHLUO+s+zoLRATWQ55AW1GkEz181TnWFwKPkQpaZSrLTl/1dWZCzisb8f43zWeTztPPls+Gf4OPjQ1bMrL21/iW1x23ht12u8tqtiKU6DzoC11hprnfpysnbCzccb14Is3PYvwMO/N2GuYXRw61Ahs39DCPNWM9ieScrBaFLQaeXvrhBCCCFUFgfzt99+O9999x2FhYXY2DRuBmhRszGdvPntcDyrjyTw/Oi2tHdtXy4JnqIoJOcnE50TTZqjE1943kmWsp/OdudpVZjMjLSz2KSegYPfqCe0sr1sGm8IeHaADreB3rb+Op1yGra9B3kp0PludT14fWVdv84oisLvEb+z8NBC4nLjzNv72QcyJeYkAyI3qBu8O8PQl6D1yKofpjj7Q48Z16DXotF1uA18w6E4F7xunLKRrVq0wlHvSE5JDqczTtPB7dJ7N5qMPL/1eQ4kH8BB78Cnwz/Fx8EHUNf/Lxy2kG9OfMOHBz6k2FRc4dxFxiKKjEVQov6ckJcAOsDBHhK3qq+Lgp2C6eDegU7unejm2Y02Lm1qla+hLgJd7bDV6ygoMRKVlkeoh+SmEUIIIYTK4jrz2dnZ9O3blzZt2rBo0SLc3d3ru2/XVHOtM1+moNhIt9fWU1Bi5NdH+1NsdY4Zf8zAzsqOQKdALmRfoKC0oMrjezmF8qFde+xj90P8AXXU90qOPnDTU2oyvasJutPOw+b56prgy2tj23tAt2nq+mBnP8vPf52JyIrg9V2vszdxLwBO1k7c3up27tJ7EfTLo2ojj3bqqGvbsVBJ+UchbjQP//UwW+O28kzPZ5jSfop5+yeHPuHTw5+i1+r574j/VrmmvrC0kPzS/HLbTIqJEmMJRcYiik3FFJUWkV2cTdrBpaSdXUOaTyfivdtxIu1EuYduZez19nT16Ep3r+5MDJuIs8G5Xt7rrR9v43BsFp/c141bOvnUyzmFEEIIcf1q8DrzTzzxBGFhYfzyyy/8/fffdO/evdo6819++aWllxL1wNZax9C2nqw+msDqowk8Nrwd1lpr8kvzOZV+CgCdRoevgy/WuHAqTsFG68yMPu347vRy9mSf5wFrOz6d/APOVnaQGa0mhEqPgPTzcGo1ZMXAmqfVeuGDnlHri5fkq6OGxXlqR/x7gVUVWa/TI2HbAjj4LShGdVvbseDdCfYvVTO2b31HHa0f/oo6pbwJKywtZNHRRSw+tphSUyk2Ohse6vIQk9tNxqYgCz7tqzbs+QCM/s+l6fRCCLp5dWNr3FYOJh80B/NHUo7w+ZHPAZjXb161yfFsrGxqVdYOgIxE2Pc9OBlh8AIA0gvTOZF2gqOpRzmccpjDyYfJLclle/x2tsdvJzonmtf6V5zGb4kwb0cOx2ZxKjFHgnkhhBBCmFk8Mq/Vas115Gu8iEaD0Wi05DLXTHMfmQdYfSSBR5YfINDVjs1zBrMncQ9nMs4Q5BREoGMgfo5+6LV6nvnxMD/si2VKnyBeu60jx1OP89BfD5FVlEWrFq1YNHIR7rZXzMQoLYIDX8OWdyA3sepOOHipNe27z1CzrYNaCm/7+2om/bKR+FYj1JHosuzbxlI4vRr2LFIztmu08I+/wL97ff+ayovdB/EHL1t7bg8GJ3W6e0113augKAqbYzfznz3/ITY3FoABfgN4sfeL+Dv6g8kE394J5zeAV0eYucHiawnRXB1MPsjUtVNxtXFl08RNFJQWMPH3iVzIvsDokNHMHzi//i6WeBQ+GwC2LvBsVKVNjCYjZzPPsjV2Kx8e/BBrrTXr71qPq43rVV9+8bZIXv39BCPbe/H51B5XfT4hhBBCXN8afGR+yZIllh4qGsmQth7Y6LVEp+dzPD6b3n696e3Tu1ybEqOJP08kATC6k1qrvIN7B5aMWsJD6x/iXOY5pq2dxiv9XqGta9tLCaCsDNDrAQifDHu/hB0fQX6qmnjN2kENgvPTIDcJNv8Htr6rjroX5ahBa5lWw2HgMxBYvl/orNQ18+1vhZ8eUKfg//JPeGhLwwS6hVnw57/hwNLK99t7Qp9/qpnhbWo/lTYqK4r/7P0P2+K2AeBp68lzvZ9jeODwS1m19/xX/Z1Y2cAdX0ogL0QlOrh1wFprTXphOheyL7Ds5DIuZF/A086TF3u/WL8Xcw0FNGopyLw0tUTgFXRaHW1d2xLmEsaG6A0cTzvOqrOrmNlp5lVf3pzRPkky2gshhBDiEotH5pubG2FkHuCfy/az9lgiDw8O5Zmb21bYv/VsClO+3IObvTW7XxiGle7SsomY7BgeWP9AubWiPvY+tHZpTWiLUHztffF18MXb3hsfO28cDVf8HkuL4dRv6uh69M5L2zVa6DABBjyhTqmvSX46fNJHfTDQ/3EY8Wpdfw3VO/MH/PaEOq0f1OUCGs3Fmu25kJ2gPqgAdZS+xwzo8zA4eld5ypziHL44+gVfn/janH17avupPNj5Qez19pcaJh6DRUPUnAS3vKM+IBFCVGra2mkcSD7AzcE3sy5qHQD/HfFf+vn2q/+LvdcJsqJhxjoI6ltt01/P/8qL217E296btRPWVsi2X1dpuUV0f/0vNBo49soo7A1Xdz4hhBBCXN8afGReNE2jO/mw9lgia44mMGdUWIUay2uOqlPkR3bwLhfIAwQ4BbD05qXM3zufwymHScpPIiEvgYS8BLbEbqlwrRDnEG4JuYUxIWMIcApQ18p3vEN9JR6Fg8tAp4ce/1Az4teWnSuMfR9W3KvOAGg7DgKqXhtbK4oCKadg6wJ11B/UTP3jP4bg/uXbGkvg2E+w7X1IOanmCNj1mZqY76bZ4OBpbnoq/RQrTq1gTeQac4LBAX4DeLbnswQ7B5c/b0kB/DRTDeTb3Aw9r35ET4jmrLtXdw4kHzAH8pPaTmqYQB7U0o9Z0ZB6psZgflTwKN7d9y6JeYlsjNnIiKARV3VpNwcD7g4GUnOLOJOUQ3igy1WdTwghhBDNw1UH8wkJCfzvf//j9OnTZGdnV7qGXhLgXT+GtvXE2kpLVFo+JxNyaO976UmP0aTw53E1mL+lU+WjzF72Xrw7+F0AsoqyOJd5jrMZZ4nMiiQhL4HEvEQS8hLILMokMiuShYcWsvDQQjq7d2Z0yGj6+PShZYuWaL07qUndLNX2FrVc3ZHv4X8PX5xuf7EsXkEGRO+GFgHVl+tSFPWhwon/qa+0s+p2jRb6PgKDXwBru4rH6fTQ5R7oNBHO/qEm5IvZDbs/hQNLKegxg/UBHfk+ajVHUo6YDwt1DuWJ7k8wyH9QhYco5KfDj/erDwfsPdWHCFWVnhNCAGoSPI6q34c4h/BE9yca7mLubdTlL6lnamxq0Bm4o/UdLDq6iO9OfXfVwTxAOx9Htp4t4nSiBPNCCCGEUF1VMP/RRx8xZ84cSkpKzNvKgvmyYEVRFAnmryMOBisGt/HgzxNJrD2WUC6Y33ImhbS8YlrY6enTsuKa0Cs5G5zp7tWd7l4Vk9BlF2ezKWYTqyNWsythF0dSj3AkVQ1snaydCPcMp5tXN7zsvMgtziWnJIfc4lyKjEUEOQXR0b0jbVzaYK2rIvM9wM1vQcQm9cP1mqfBORDO/QVx+y4l0gvsq45wtxuvzgxQFLW03on/wYlfISPy0vl01uqU+oFPg38tkkxptRA2GtrcjHL+b45teoWfC2JYG/8/chN/A8BKY8XwoOFMDJtID68eFYN4gITD8P1ktUKA3g7u+AIcPGq+vhA3uK4eXbG1sqXEWMKbA97E1sq24S7m3kr9mnauVs0nhk1k8bHF7E3cy5mMM7RxaXNVlw/zcmTr2VROJcq6eSGEEEKoLA7mN2zYwOOPP46TkxNPPfUUmzdvZufOnfz3v//lzJkzrFq1iqioKJ544gm6dOlSn30WV+mWTj78eSKJ1UcTmD2iDRqNhq1nU3h0+QHzfr3u6mqZO1k7MT50PONDx5NakMofUX+wMXojR1KPkF2czebYzWyO3VztOay0VrRxaUOYSxg+Dj742Pvga++Lj70Pfo5+aO1cYdwH8N096pT9y7m2VIPj6J3qy8FLTa4XuUUtoWe+iI26vf1t0GYU2NQ+X0JeSR6HUw5zIOkAG6I3cE6fCXo1UZVfSSkTcnKZ0HoC7je9VXVZucPfw2+PQWkhuITAPd9WP5tACGHmYO3AkpuXgKIm6mxQ7heD8VqMzAN423szNHAo6y+s57tT3zG379yrunxYWRI8CeaFEEIIcZHFCfDGjx/P6tWr2bFjB71792bGjBl8/fXX5hJ0RUVF/POf/2TVqlUcOHCAli1b1vkaCxcu5O233yYxMZEuXbrw0Ucf0atXryrbr1y5kn//+99ERUXRunVr/vOf/3DLLbfU6lo3SgI8gJzCErq/9hfFRhN/PDGQ4/FZPPPjEUpNCn1buvHfqd1xstE3yLVLTCWcTj/N/qT9HEo+RE5xDg7WDjjoHXC0dkSv1XMm8wzHU4+TWZRZ5Xk87TwZFjiMEUEjCD/4E1bHflRH4VsNh1bDwNkfchJh/1ewbzEFeclkabV4GY1o9PbQZqSaGb/VCDA41Nhvk2IiOjuaY2nHOJZ6jANJBzidcRpT2QwA1Km1I4JGcHvorfSI3IN2/b/VHe3GwYRFl5YBAGTFqhn99y1Wf241Au5YpJa+EkJcf3IS4d0wdRnOi4lqBY8a7E3cy/1/3I+tlS3r71yPs6H2lS+udCwui7EfbcPFTs+Bf4+ofJaPEEIIIZqF2samFgfz3t7eBAQEsHfvXoAKwTyoAX1QUBA333wzX331VZ3O//333zN16lQ+++wzevfuzfvvv8/KlSs5ffo0np6eFdrv2LGDgQMH8uabbzJ27FiWL1/Of/7zHw4cOEDHjh1rvN6NFMwDzFy6l79OJtPex4kTCdkAjO/iy9t3dcZgVcUo8jWkKArxefEcSz1GZFakeS1+Ql4C8bnxFBmLzG1dbVzp49MHVxtXHK0dcdA74GDtQFJeEmcyznA24wzROdEoQIDBleEtxzA85GY6uXeq9AOxoigk5CVwLPUYx9OOczz1OCfSTpBTUnFEzM/Bj3DPcHp692R40HCcrC/7b+fYKvj5ITWhXUAfuPc7SDyiZvM/vebSUoCBc2Dw81WP3gshGp+iwJsBUJwDD+8Gz4rVQCoeonDHb3dwNuMsT/d4mmkdpll8+cISI+1fXodJgT0vDMPTSUpWCiGEEM1VgwfzBoOB22+/nRUrVgDw4IMP8uWXX5Kbm4ut7aURyLvuuotdu3YRExNT1akq1bt3b3r27MnHH38MgMlkIiAggH/9618899xzFdrffffd5OXl8fvvv5u39enTh65du/LZZ59VaF9UVERR0aWAMDs7m4CAgBsmmP9pfyxPrTxs/vmhgS159ua2aLXX/2hPsbGYXQm7WH9hPRtjNpJVlFWr4zRoULj0n7uXnRft3Nqh4dJ7Liwt5HTGadIL0yscb9AZaOvalg5uHejq2ZVwz3C87asuRwdA1Db4bhIUZYHOAJc9hCD4JjX7fejQWvVfCNHIPh+i5tzw7wX2tctr8aMxnXnGOFzQ0VVjX/MB1UjPK6LUpGCt00p+TCGEEMJCM3v+m5u6jW/sblSrwUvTubq6kpeXZ/7ZxUWdHhwdHU1YWJh5u9FoJC0trU7nLi4uZv/+/Tz//PPmbVqtluHDh7Nz585Kj9m5cyezZ88ut23UqFH88ssvlbZ/8803mTdvXp361ZwMb++FnbWOghIj/x7TnvsH1KE0XCOz1lkz0H8gA/0HUmIqYV/iPo6nHSenOKdcMj1XG1dau7SmtUtr2ri0wc7Kjq1xW/nrwl9sid1CUn4SSflJlV7DSmNFa5fWdHDvQEe3jnRw70Boi1D02jouPwgeAPevg2/vhOw4sHZQM+H3nAme7erhtyGEuGZ8w9VgPnZPrQ8Zo9HwYYAvGTrYqGRf3fUrKa4hhBBCiLpJzYpv7C7UG4uD+cDAwHKj7R07dkRRFH7//XdzMJ+bm8vWrVvx9/ev07lTU1MxGo14eXmV2+7l5cWpU6cqPSYxMbHS9omJiZW2f/7558sF/2Uj8zcKZ1s9K2f1pcSo0DWgRWN3x2J6rZ6+vn3p61t93ecyo4JHMSp4FEXGInYn7K4QzFtprAhtEUqYaxgGXc1rYmvFqz08uBkubFdH4euQZE8IcR0Z9rJa6aK0qOa2F9kCSwrTOJAfd9WXLzaaSMgqxGTZhDohhBBCAB1a9mnsLtQbi4P5QYMG8d5775GUlISXlxdjxozB3t6eF154gcTERAIDA1m6dCnp6encc8899dnnemEwGDAY6ilYa6I6+FqejKmpM+gMDPQfeO0u6OABHW67dtcTQtQ/2xbQdVKdDwu9+BJCCCGEqE8WB/N33XUXBw8e5NChQ4waNQpXV1cWLFjArFmzWLBgAaAm/wkODq7zdHZ3d3d0Oh1JSeVHTZOSkvD2rnyNsre3d53aCyGEEEIIIYQQTZXFxcR79uzJ+vXrGTVqlHnbAw88wN69e3nmmWeYOXMmCxYs4ODBg7i6utbp3NbW1nTv3p0NGzaYt5lMJjZs2EDfvpVPp+7bt2+59gDr16+vsr0QQgghhBBCCNFUWTwyX5Vu3brRrVu3qz7P7NmzmTZtGj169KBXr168//775OXlMWPGDACmTp2Kn58fb775JgCPP/44gwYN4t1332XMmDGsWLGCffv28fnnn9fqemVJ/bOzrzJBkRBCCCGEEEIIYaGymLSmwnP1HszXl7vvvpuUlBRefvllEhMT6dq1K+vWrTMnuYuOjkarvTSxoF+/fixfvpyXXnqJF154gdatW/PLL7/UqsY8QE6OWkP8RkqCJ4QQQgghhBDi+pSTk4Ozc9V5ziyuM9/cmEwm4uPjcXR0RHOdF/Aty7wfExNTbd1BcX2T+9g8yH1sHuQ+Ng9yH5sPuZfNg9zH5kHu47WnKAo5OTn4+vqWG8C+0nU7Mn+tabXaOpfQa2xOTk7yD6oZkPvYPMh9bB7kPjYPch+bD7mXzYPcx+ZB7uO1Vd2IfBmLE+AJIYQQQgghhBCicUgwL4QQQgghhBBCNDESzDdBBoOBuXPnYjAYGrsr4irIfWwe5D42D3Ifmwe5j82H3MvmQe5j8yD38folCfCEEEIIIYQQQogmRkbmhRBCCCGEEEKIJkaCeSGEEEIIIYQQoomRYF4IIYQQQgghhGhiJJgXQgghhBBCCCGaGAnmr1MLFy4kODgYGxsbevfuzZ49e6ptv3LlStq2bYuNjQ2dOnVizZo116inojp1uY+LFi3ipptuwsXFBRcXF4YPH17jfRfXRl3/PZZZsWIFGo2G2267rWE7KGqlrvcxMzOTRx55BB8fHwwGA23atJG/rdeBut7H999/n7CwMGxtbQkICODJJ5+ksLDwGvVWVGbLli2MGzcOX19fNBoNv/zyS43HbNq0iW7dumEwGGjVqhVfffVVg/dTVK+u93HVqlWMGDECDw8PnJyc6Nu3L3/88ce16ayoliX/Jsts374dKysrunbt2mD9E1WTYP469P333zN79mzmzp3LgQMH6NKlC6NGjSI5ObnS9jt27ODee+/lH//4BwcPHuS2227jtttu49ixY9e45+Jydb2PmzZt4t5772Xjxo3s3LmTgIAARo4cSVxc3DXuubhcXe9jmaioKJ5++mluuumma9RTUZ263sfi4mJGjBhBVFQUP/74I6dPn2bRokX4+fld456Ly9X1Pi5fvpznnnuOuXPncvLkSb788ku+//57XnjhhWvcc3G5vLw8unTpwsKFC2vVPjIykjFjxjBkyBAOHTrEE088wcyZMyUQbGR1vY9btmxhxIgRrFmzhv379zNkyBDGjRvHwYMHG7inoiZ1vZdlMjMzmTp1KsOGDWugnokaKeK606tXL+WRRx4x/2w0GhVfX1/lzTffrLT9xIkTlTFjxpTb1rt3b+Whhx5q0H6K6tX1Pl6ptLRUcXR0VJYuXdpQXRS1YMl9LC0tVfr166d88cUXyrRp05Rbb731GvRUVKeu9/HTTz9VWrZsqRQXF1+rLopaqOt9fOSRR5ShQ4eW2zZ79mylf//+DdpPUXuA8vPPP1fb5plnnlE6dOhQbtvdd9+tjBo1qgF7JuqiNvexMu3bt1fmzZtX/x0SFqvLvbz77ruVl156SZk7d67SpUuXBu2XqJyMzF9niouL2b9/P8OHDzdv02q1DB8+nJ07d1Z6zM6dO8u1Bxg1alSV7UXDs+Q+Xik/P5+SkhJcXV0bqpuiBpbex1dffRVPT0/+8Y9/XItuihpYch9//fVX+vbtyyOPPIKXlxcdO3bkjTfewGg0XqtuiytYch/79evH/v37zVPxIyIiWLNmDbfccss16bOoH/I5p3kymUzk5OTI55wmasmSJURERDB37tzG7soNzaqxOyDKS01NxWg04uXlVW67l5cXp06dqvSYxMTEStsnJiY2WD9F9Sy5j1d69tln8fX1rfABRlw7ltzHbdu28eWXX3Lo0KFr0ENRG5bcx4iICP7++2/uu+8+1qxZw7lz53j44YcpKSmRDy6NxJL7OGnSJFJTUxkwYACKolBaWsqsWbNkmn0TU9XnnOzsbAoKCrC1tW2knomr8c4775Cbm8vEiRMbuyuijs6ePctzzz3H1q1bsbKScLIxyci8ENeht956ixUrVvDzzz9jY2PT2N0RtZSTk8OUKVNYtGgR7u7ujd0dcRVMJhOenp58/vnndO/enbvvvpsXX3yRzz77rLG7Jupg06ZNvPHGG3zyySccOHCAVatWsXr1al577bXG7poQN7Tly5czb948fvjhBzw9PRu7O6IOjEYjkyZNYt68ebRp06axu3PDk0cp1xl3d3d0Oh1JSUnlticlJeHt7V3pMd7e3nVqLxqeJfexzDvvvMNbb73FX3/9RefOnRuym6IGdb2P58+fJyoqinHjxpm3mUwmAKysrDh9+jShoaEN22lRgSX/Hn18fNDr9eh0OvO2du3akZiYSHFxMdbW1g3aZ1GRJffx3//+N1OmTGHmzJkAdOrUiby8PB588EFefPFFtFoZ02gKqvqc4+TkJKPyTdCKFSuYOXMmK1eulNmHTVBOTg779u3j4MGDPProo4D6WUdRFKysrPjzzz8ZOnRoI/fyxiH/F7vOWFtb0717dzZs2GDeZjKZ2LBhA3379q30mL59+5ZrD7B+/foq24uGZ8l9BJg/fz6vvfYa69ato0ePHteiq6Iadb2Pbdu25ejRoxw6dMj8Gj9+vDkDc0BAwLXsvrjIkn+P/fv359y5c+aHMQBnzpzBx8dHAvlGYsl9zM/PrxCwlz2gURSl4Tor6pV8zmk+vvvuO2bMmMF3333HmDFjGrs7wgJOTk4VPuvMmjWLsLAwDh06RO/evRu7izeWRk7AJyqxYsUKxWAwKF999ZVy4sQJ5cEHH1RatGihJCYmKoqiKFOmTFGee+45c/vt27crVlZWyjvvvKOcPHlSmTt3rqLX65WjR4821lsQSt3v41tvvaVYW1srP/74o5KQkGB+5eTkNNZbEErd7+OVJJv99aGu9zE6OlpxdHRUHn30UeX06dPK77//rnh6eiqvv/56Y70FodT9Ps6dO1dxdHRUvvvuOyUiIkL5888/ldDQUGXixImN9RaEoig5OTnKwYMHlYMHDyqAsmDBAuXgwYPKhQsXFEVRlOeee06ZMmWKuX1ERIRiZ2enzJkzRzl58qSycOFCRafTKevWrWustyCUut/Hb7/9VrGyslIWLlxY7nNOZmZmY70FcVFd7+WVJJt945Fg/jr10UcfKYGBgYq1tbXSq1cvZdeuXeZ9gwYNUqZNm1au/Q8//KC0adNGsba2Vjp06KCsXr36GvdYVKYu9zEoKEgBKrzmzp177Tsuyqnrv8fLSTB//ajrfdyxY4fSu3dvxWAwKC1btlT+7//+TyktLb3GvRZXqst9LCkpUV555RUlNDRUsbGxUQICApSHH35YycjIuPYdF2YbN26s9P93Zfdu2rRpyqBBgyoc07VrV8Xa2lpp2bKlsmTJkmveb1FeXe/joEGDqm0vGo8l/yYvJ8F849EoiswzE0IIIYQQQgghmhJZMy+EEEIIIYQQQjQxEswLIYQQQgghhBBNjATzQgghhBBCCCFEEyPBvBBCCCGEEEII0cRIMC+EEEIIIYQQQjQxEswLIYQQQgghhBBNjATzQgghhBBCCCFEEyPBvBBCCCGEEEII0cRIMC+EEEJcJioqCo1GQ3BwcGN3pdEEBwej0WjMr+HDhzd2l5qkwYMHo9Fo2LRp01Wdx8bGptz9mD59er30TwghRNMmwbwQQogbSlmgGhUV1dhdaTCvvPIKGo2GV1555arOc8cddzBt2jRGjRpVPx0TFpkyZQrTpk2jf//+jd0VIYQQ1xGrxu6AEEIIcT3x8/Pj5MmT6PX6xu5Ko3vnnXdu6BkK14tFixYB8NVXX7F9+/ZG7o0QQojrhQTzQgghxGX0ej1t27Zt7G4IIYQQQlRLptkLIYS4IXz11VdoNBouXLgAQEhISLl1yGXrmqtbM1/WFmDZsmX06tULBwcHPDw8uPfee4mOjgZAURQ+/vhjunbtir29Pe7u7kyfPp3k5OQq+3fmzBkeeughQkNDsbGxwdnZmYEDB7Js2bI6vU+NRsO8efMAmDdvXoOttd6/fz933303/v7+WFtb4+TkRMuWLbnjjjv43//+V+Ux9913H4GBgRgMBlxdXRk1ahRr1qyp8jqlpaUsXryY4cOH4+7ujsFgwN/fn+HDh/PRRx9V2v6zzz6jX79+ODs7Y2NjQ+vWrXnssceIi4ur9BqX39effvqJAQMG4OTkhL29Pf3796+2fzExMdx///34+PiYr/Xiiy9SUFBQ5TFZWVm89NJLdOrUCXt7ewwGA76+vvTv35+XX36ZkpKSKo8VQgghysjIvBBCiBtCq1atmDZtGj/++CN5eXnccccdODg4mPd7e3vX+lzPP/8877zzDgMHDmT06NHs2bOHFStWsH37dg4fPsysWbP49ddfGTx4MC1btmT79u0sXbqUgwcPsnfvXqytrcudb+XKlUydOpXCwkLatm3LLbfcQlZWFrt372bKlCn8/fffLF68uFZ9mzZtGocOHeLw4cN06dKFrl27mvcNGDCg1u+xOhs2bGD06NGUlJTQpUsX+vbti9FoJC4ujtWrV2M0Grn11lvLHfPBBx8we/ZsTCYTXbt2pXfv3iQmJrJp0yb+/PNP5s2bx8svv1zumKysLMaOHcu2bdvQ6/X069cPX19fEhMTOXLkCBs2bOBf//qXuX1RURFjx47lr7/+wsbGhiFDhuDk5MSOHTv46KOP+O677/jjjz/o1q1bpe9r7ty5vPbaa/Tr149bbrmFU6dOsWPHDsaOHctPP/3E7bffXq79qVOnGDRoEMnJyfj4+DB+/Hjy8vJ477332LhxY6XXyM/PZ8CAARw7dgwPDw+GDRuGvb09iYmJ5uvNnj2bFi1aWHBnhBBC3FAUIYQQ4gYSFBSkAEpkZGSl+yMjIxVACQoKqrAPUADFzc1NOXTokHl7fn6+MmDAAAVQOnXqpISGhipRUS/TqsYAAJ8xSURBVFHm/SkpKUqrVq0UQFm2bFm5cx45ckQxGAyKjY2N8tNPP5XbFxUVpXTq1EkBlKVLl9b6Pc6dO1cBlLlz59b6mMvV9DsaMmRIpe9FURQlMzNT2blzZ7lt69atUzQajeLu7q5s3ry53L4jR44o/v7+CqBs2rSp3L4JEyYogBIeHl6hLyUlJcovv/xSbtuzzz6rAEpoaGi59sXFxco//vEPBVBCQkKUoqKicseV3dcWLVoou3btKrev7HfZpk2bCu+1Z8+eCqBMnDhRKSgoMG+/cOGCEhoaaj7vxo0bzfuWLl2qAMro0aOV4uLicuczGo3Kpk2bKvSvzJIlSxRAmTZtWqX7hRBC3Fhkmr0QQghRR6+++ipdunQx/2xra8vs2bMBOHr0KB9++CFBQUHm/e7u7vzzn/8E1FHty/3f//0fRUVFvP7660yYMKHcvqCgIL788ksAPvzwwwZ5L5ZISkoC4JZbbqmwz9nZmT59+pTbNnfuXBRF4bPPPmPgwIHl9nXq1IkFCxYAlJs2f/jwYVatWoWNjQ2//fZbhWUPVlZW5Ub/CwsLWbhwIQDvvfdeufZ6vZ4PP/wQLy8vIiMj+fHHHyt9X6+++iq9e/cut+3555/H2dmZM2fOEBMTY96+fft29u7di729PZ988gk2NjbmfYGBgbzzzjuVXqPsdzdixIgKSRa1Wi2DBg2qMHNDCCGEqIwE80IIIUQdVRbEtm7dGlCDzJEjR1a5Pz4+3rzNZDKxdu1aAO6+++5Kr9WjRw8cHBw4ePAghYWFV933+tCrVy8A7rvvPrZt20ZpaWmVbVNTU9mzZw+2traMGzeu0jaDBw8GYMeOHeZt69atA2DMmDH4+fnV2Kd9+/aRm5uLq6trpdexs7PjnnvuAahyCnxlxxkMBlq2bAlQbs19WY6Fm2++GTc3twrH3XrrrTg7O1fY3rNnTwDmz5/P119/TXp6eg3vTAghhKicBPNCCCFEHQUGBlbYVrb+3sfHByuriilpHB0dAcoF5GlpaWRnZwMQEBBQLlld2Uur1ZKbm4vJZCItLa0h3k6dvfnmm3Tr1o21a9dy00034eTkxIABA3jppZc4efJkubaRkZEoikJBQQEGg6HS9+jp6QlASkqK+biyRIW1rSxQFmiHhIRU2SY0NLRc2ytVdl8BnJycgPL3LjY2ttrrVZVEcfDgwTz77LMkJyczbdo03N3dCQsL4/777+d///sfJpOpyv4LIYQQl5MEeEIIIUQdabVVPwuvbt+VLg/cpk2bVmN7g8FQ63M3JG9vb/bt28fmzZv566+/2L59O7t372b79u288cYbvPnmmzz77LPApffo4ODAHXfc0ZjdrlFd7t3VeOutt5g1axa//fYb27ZtY/v27SxZsoQlS5bQs2dPNm7ciL29/TXpixBCiKZLgnkhhBCikbi7u2Nra0tBQQHvvPMO7u7ujd2lWtNoNAwePNg8Rb6wsJCvvvqKRx55hBdeeIE777yT0NBQAgICzO0XL15c64C5bJT81KlTtWpfNhU/MjKyyjYRERHl2l6NsnNERUVV2aZsdkFlgoOD+de//mXOxr93714mT57M3r17mT9/vrm8oBBCCFEVmWYvhBDihlKWXKy6dd7Xik6nY8SIEQD88MMP9XbexniPNjY2zJo1i86dO2MymThy5AgAvr6+dO7cmZycHPM6+Nq4+eabAVizZk25PANVKcstkJ6ezq+//lphf0FBAStWrABgyJAhte5HVQYNGgSoa/srW/f+66+/kpmZWevz9ezZk4cffhiAQ4cOXXX/hBBCNH8SzAshhLih+Pv7A3D8+PFG7olq7ty5WFtbM2fOHJYuXVrpmuljx46xatWqWp+zod/jO++8Q3R0dIXtp06d4uzZswDlsvm//vrrAMyYMYPffvutwnGKorB7927+/PNP87auXbty6623UlBQwK233lrheqWlpeWCdhsbGx555BEAnnrqqXKj4iUlJTz++OMkJiYSEhLCnXfeacnbLuemm26iW7du5Obm8sgjj1BUVGTeFxMTw9NPP13pcT///DNbtmypcJ9LSkrMDzsu/90JIYQQVZFp9kIIIW4od9xxBxs3bmTy5MmMHDkSFxcXAObMmUNYWNg170+3bt1YtmwZ06dPZ/r06bz00ku0b98eDw8P0tPTOXr0KLGxsdx9990VStdVZdSoUdjb2/PLL78wYMAAWrdujU6no3///syYMeOq+/z6668zZ84c2rZtS7t27bC1tSU+Pt6c2X7q1Kl069bN3H7cuHF88MEHPPXUU4wfP55WrVoRFhaGs7MzKSkpHD58mOTkZJ599tlylQCWLFnCLbfcwq5du2jdujX9+vXD19eXxMREjh49SkpKCoqimNvPmzePffv2sWHDBtq1a8eQIUNwdHRk586dREdH4+bmxsqVK+ut9Ns333zD4MGDWbFiBVu2bGHAgAHk5+fz999/07lzZ9zd3dm5c2e5YzZv3swHH3yAu7s74eHheHp6kpOTw65du0hOTsbPz49nnnmmXvonhBCieZNgXgghxA3ln//8Jzk5OSxbtow1a9aYM5RPnjy5UYJ5gLvuuouePXvy4Ycfsn79erZv347RaMTLy4tWrVrx6KOP1mk02cvLi7Vr1/Lqq6+yf/9+du7ciclkorS0tF6C+YULF7Jhwwb27t3L5s2bycvLw9vbmxEjRvDggw+Wq/9e5rHHHmPo0KF89NFHbNy4kQ0bNqDVavH29iY8PJwxY8ZUSJDn4uLC5s2bWbx4McuXL+fQoUPs2LEDT09Punbtym233VauvcFgYN26dSxatIivv/6arVu3UlRUREBAAP/617949tln62W9fJn27duzb98+5s6dy9q1a/nll1/w9/fnX//6Fy+//HKlJQynT5+Ora0t27Zt48SJE2zevBlnZ2cCAwN54oknePDBBystdSeEEEJcSaNc/khbCCGEEDe84OBgLly4QGRkZKXl1UTj+Oqrr5gxYwbTpk3jq6++auzuCCGEaGQyMi+EEEKISj399NM4ODjQoUMH5syZ09jduWE98MADlJSUcO7cucbuihBCiOuIBPNCCCGEqNRPP/0EwLBhwySYb0TffPNNuQR7QgghBMg0eyGEEEIIIYQQosmR0nRCCCGEEEIIIUQTI8G8EEIIIYQQQgjRxEgwL4QQQgghhBBCNDESzAshhBBCCCGEEE2MBPNCCCGEEEIIIUQTI8G8EEIIIYQQQgjRxEgwL4QQQgghhBBCNDESzAshhBBCCCGEEE2MBPNCCCHEDWT69OloNBq++uqrxu5KvYuLi8PFxQWNRoOVlVVjd0cIIYRoUBLMCyGEEKJZeOCBB8jKymrsbgghhBDXhATzQgghhGjyvvjiC9auXcsjjzzS2F0RQgghrgkJ5oUQQgjRpF24cIHZs2fTp08fnnzyycbujhBCCHFNSDAvhBBCNHH5+fm8//77DBgwABcXFwwGA0FBQYwbN47ly5fX6hw5OTksWrSICRMm0Lp1a+zt7bG3t6dTp068+OKLZGZmVnpcQkICjz/+OG3atMHGxgY7OzsCAgIYNmwY77zzToX2f/31F+PGjcPLywu9Xo+LiwutW7dm8uTJbNmypc7vXVEU7r//foqLi1m8eDFarXy0EUIIcWOQ7DBCCCFEExYTE8PNN9/MiRMnsLOzo3///ri5uREXF8fWrVs5evQokyZNqvE8hw8f5sEHH8TDw4OwsDC6d+9ORkYG+/fv54033uCHH35g165duLm5mY9JTEykR48exMfHExgYyM0334yNjQ3x8fEcOnSI/fv38/TTT5vbL126lBkzZgDQq1cvhgwZQkFBAbGxsaxYsQJ3d3cGDhxYp/f/ySef8Pfff/PGG2/Qrl07oqKi6nS8EEII0VRJMC+EEEI0USaTiQkTJnDixAlGjhzJsmXL8PDwMO8vLCzk77//rtW5goOD+euvvxgyZEi50e38/Hz++c9/8vXXX/Pyyy+zcOFC877PP/+c+Ph4HnzwQT777DM0Go15X0lJSYWR9nnz5qEoClu3bmXAgAHl9iUnJxMXF1en93/+/HmeffZZunfvzpw5c+p0rBBCCNHUyVw0IYQQoon67bff2LdvHz4+Pvz000/lAnkAGxsbbrnlllqdy9/fn2HDhlWYpm5nZ8enn36KlZUVK1euLLcvKSkJgJtvvrlcIA+g1+sZNmxYhfbOzs4VAnkAT09PwsPDa9VXUB9kTJ8+neLiYpYsWSKl6IQQQtxw5P98QgghRBO1bt06ACZNmoSDg0O9nHPHjh1s3bqV6Oho8vPzURQFAGtra1JSUsjIyMDFxQVQp8p/8sknPPfccyiKwsiRI6vtR69evdi0aRNTp07l8ccfJzw83OI17u+//z7btm1j3rx5dOrUyaJzCCGEEE2ZBPNCCCFEE3XhwgUA2rZte9XnSk5O5o477mDbtm3VtsvOzjYH81OmTGH9+vV8++233HHHHeh0Otq3b8+AAQO48847GTp0aLljP/nkE8aOHcs333zDN998g6OjIz179mTo0KFMmTKFwMDAWvX19OnTvPjii3Tp0oXnn3/esjcshBBCNHEyzV4IIYQQzJw5k23bttG3b1/+/PNPkpKSKC4uRlEUFEXBx8cHwDxSD6DValm2bBnHjx9n/vz5jB07loSEBD799FOGDRvG+PHjMRqN5vbt2rXj9OnTrF69mqeeeoqOHTuydetWXnrpJVq3bs2yZctq1de1a9dSWFhIXl4eI0aMYPDgwebXPffcA4DRaDRvK5vBIIQQQjQnMjIvhBBCNFFlI9mnTp26qvPk5eWxZs0atFota9asoUWLFhX2JyYmVnl8+/btad++PXPmzEFRFP7++28mTZrEb7/9xtdff23OYA9gZWXFLbfcYl7Ln52dzYIFC5g3bx4PPfQQt99+O/b29rXq97lz5zh37lyV+zdv3gzA9OnTa3U+IYQQoimRkXkhhBCiibr55psB+O6778jLy7P4PFlZWRiNRpycnCoE8gDLli0rNyJfHY1Gw7Bhw8zl8A4dOlRteycnJ1555RVatGhBfn4+Z86cqfEaTzzxhHnGwJWvyMhIAHQ6nXmbBPNCCCGaIwnmhRBCiCZq/PjxhIeHEx8fz1133UVaWlq5/YWFhaxdu7bG83h5eeHi4kJmZibffPNNuX27du2qcl36119/zf79+ytsz8nJYdOmTQAEBQUBaom7BQsWkJKSUqH91q1byczMRKfT4e/vX2N/hRBCCCHT7IUQQogmS6vV8vPPPzNq1CjWrl1LYGAgAwYMwM3Njbi4OA4fPkyLFi2Iioqq9jw6nY6XX36ZJ598kqlTp7Jw4UJatmxJdHQ0O3bsYPLkyWzZssWccK/MqlWrmDZtGr6+vnTt2hUXFxcyMjLYvn07WVlZdOzYkQceeACA4uJinnrqKebMmUOnTp1o3bo1er2eqKgodu3aBcCLL75YobyeEEIIISonwbwQQgjRhAUFBbFv3z4++eQTfvzxR3bu3ElxcTHe3t4MGjTIPN29Jk888QQhISHMnz+fEydOcPz4cdq2bcvChQuZNWsWISEhFY556qmnCAkJYceOHRw4cID09HRcXV1p3749kyZNYsaMGeb17w4ODnz22Wds3ryZgwcPsn79eoqLi/H19WXChAk8/PDDFbLfCyGEEKJqGqW2i+CEEEIIIYQQQghxXZA180IIIYQQQgghRBMjwbwQQgghhBBCCNHESDAvhBBCCCGEEEI0MRLMCyGEEEIIIYQQTYwE80IIIYQQQgghRBMjwbwQQgghhBBCCNHESJ35i0wmE/Hx8Tg6OqLRaBq7O0IIIYQQQgghbkCKopCTk4Ovry9abdXj7xLMXxQfH09AQEBjd0MIIYQQQgghhCAmJgZ/f/8q91+XwfyWLVt4++232b9/PwkJCfz888/cdttt1R6zadMmZs+ezfHjxwkICOCll15i+vTptb6mo6MjoP7CnJycrqL3QgghhBBCCCGEZbKzswkICDDHqFW5LoP5vLw8unTpwv3338+ECRNqbB8ZGcmYMWOYNWsW3377LRs2bGDmzJn4+PgwatSoWl2zbGq9k5OTBPNCCCGEEEIIIRpVTcu/r8tgfvTo0YwePbrW7T/77DNCQkJ49913AWjXrh3btm3jvffeq3Uw35SYTAoFJUbsDdfl7RNCCCGua4qiSH4cIYQQTV6zyGa/c+dOhg8fXm7bqFGj2LlzZ5XHFBUVkZ2dXe7VFKw7lkirF9dw/1d7G7srQgghbiR7FsF7nSD5ZGP35KocTD7I4B8G88XRLxq7K0IIIcRVaRbBfGJiIl5eXuW2eXl5kZ2dTUFBQaXHvPnmmzg7O5tfTSX5nZONFSYF0vOKG7srQgghbiTHfoKsaNjzeWP35KpsjtlMemE6Hxz4gCXHljR2d4QQQgiLNYtg3hLPP/88WVlZ5ldMTExjd6lWXOytAQnmhRBCXGM5CerXE7+CsbRx+3IVkvOTzd8v2L+AFadWNGJvhBBCCMvVy6LrrKws9u7dS0pKCkFBQfTr168+Tltr3t7eJCUllduWlJSEk5MTtra2lR5jMBgwGAzXonv1yu1iMJ+RX4zJpKDVypo/IYQQDUxRICdR/T4/FS5sg5aDG7VLlkrKVz8vtHdrz4m0E/zf7v/D1sqWW1vd2sg9E0IIIermqkbmc3JymDlzJp6enowaNYrJkyfzxReX1qB98cUX+Pr6snv37qvuaHX69u3Lhg0bym1bv349ffv2bdDrNoaykXmTAlkFJY3cGyGEEDeEwkwoLbz08/GfG60rV6ssmH+6x9Pc1+4+AF7e8TJ/Rv3ZmN0SQggh6sziYL6goIDBgwezePFiXFxcGD16NIqilGszduxYkpKS+OWXX+p07tzcXA4dOsShQ4cAtfTcoUOHiI6OBtQp8lOnTjW3nzVrFhERETzzzDOcOnWKTz75hB9++IEnn3zS0rd33dLrtDjZqBMq0mSqvRBCiGuhbFS+TBOdaq8oinmavbedN8/0fIYJrSdgUkw8u/VZ4nLjGrmHQgghRO1ZHMwvWLCAgwcPcu+993L+/Hl+//33Cm28vb1p164dGzdurNO59+3bR3h4OOHh4QDMnj2b8PBwXn75ZQASEhLMgT1ASEgIq1evZv369XTp0oV3332XL774olmWpQNwlXXzQgghrqXsePWrexuwc4OCdIja0rh9skB2cTYFpWpiXE97T7QaLS/3eZmuHl0pNZWyNnJtI/dQCCGEqD2L18z/P3v3HR5F1T1w/Lslu5veewgphN57k947oqAiomLv8rO/+mLX196woQKKCmIBpUmRDtJr6KQA6b2XTXZ+fwwJhCSQbCpwPs+zT5LdmblnMoTdM/fecxctWoSPjw/ffvstJpOp0u2aN2/Ozp07q3XsAQMGlOvlv9i8efMq3Gffvn3Vaudq5WZvIColl9ScgoYORQghxPWgpGfeOQCa9oE9c9Wh9qGDGjauaioZYu9idMGoU+vm6LQ6xjUbx/6k/fwd9Tf3tLunIUMUQgghqszqnvnTp0/TvXv3yybyAHZ2diQnJ1vbjKiAm736ASQ1R+bMCyGEqAcllewd/aDNRPX7o8ug+Op6H0rIUZN5b7uyy9kOCRyCTqPjWOoxojKiGiAyIYQQovqsTuZ1Oh1m85XfxM+dO4e9vb21zYgKuJcOs5eeeSGEEPWgpGfe0Uftmbf3VIfaR15dQ+1L5st725dN5l1NrvT07QnAqqhV9R6XEEIIYQ2rk/nQ0FAOHDhAUVHlBXCys7M5ePAgrVq1srYZUQE3BzWZlwJ4Qggh6kVpz7wP6PTQapz681VW1b5kmL2XnVe514YHqXV2/o76u15jEkIIIaxldTI/btw44uLieP311yvd5vXXXycjI4OJEyda24yogJudFMATQghRj0qTeV/1a+lQ+7+uqqH2Jcn8pcPsAQYFDkKv1XMq/RSn0k7Vd2hCCCFEtVmdzD/55JP4+/vz2muvMWHCBH766ScAEhIS+P3337nlllt49913CQoK4oEHHqi1gIVUsxdCCFHPSofZn0/mm/YGey91/fmIjQ0WVnVdLpl3NjrTx68PAH9HS++8EEKIxs/qZN7FxYVVq1YRHBzMn3/+ybRp09BoNKxatYqbb76ZX375hcDAQP766y+ZM1/LSobZSzIvhBCizlksF5J5p/PJvFYHra++ofaVFcArUTLUflXkqsuuqiOEEEI0BlYn8wCtW7fm8OHDfP7554wePZpWrVrRokULhgwZwgcffEB4eDitW7eurVjFee7SMy+EEKK+5CaDUgxo1N74EhcPtS/MbZDQqqu0Z96+4mR+YJOBGLQGojKjOJF2oj5DE0IIIarN6nXmS5hMJh544AEZSl+PXO0uFMBTFAWNRtPAEYnrWvpZKC4E99CGjkQIURdK5ss7eKnF70oE9gaXppAeDYd/g87TGia+Kso155JVmAVU3jPvYHCgr39f/jn7D6uiVtHCrUV9hiiEEEJUS4165kXDcD8/zL6wyEJOYXEDRyOuK4oCiUdh1zfw6wz4oA181BY+7QxfD4Dd30F+Zp01b1EsFBbLiBQh6lXmRZXsL6bVQte71O93f1u/MVmhZFk6ext7HAwOlW43IngEoFa1l6H2QgghGrMa98yL+mdn0GOy0ZJvtpCaXYiDUS6jqGNFBRC+BHZ8AbH7yr6m0YFGqz4fuw/+/g+0uRG63g0BXardVFZhFvsS97E3YS97EvYQnxtPXlEe+UX5FBQXABDkFEQX7y6lDz8Hv1o4SSFEhS6tZH+xTtNg/Zvq337MXvDvXL+xVcPllqW7WP+A/ph0Js5mneVI6hHauLepj/CEEEKIaqtyFhgSEmJ1IxqNhtOnT1u9vyjP3d5ITHoeqbmFBLrbNXQ44lqVnaj2tu/6FnLUXi10RmjaSx1iG9gT/LtAUT4cWAh750PyCdi/QH0EdIMeD0Dr8aCzqbAJRVE4nnacNdFr2HxuM8fTjmNRLJcNKyoziqjMKH47+RsA/g7+DA4czNCmQ2nv2R6tRgYdCVFrLq1kfzF7D2g9AQ79ovbO12Eyv+F4Ikv2xfDK+LY421b8/8nllPTMVzbEvoSdjR39AvqxOno1f0f+Lcm8EEKIRqvKyXxUVFSFz2s0mkqHoZW8JnO6a5+rvY2azOcUNHQo4lpkKYadc+Cf16AwW33O0Re63QNd7lQ/wF/M6AC9H4FeD8PZHbB7LoT/Dud2qY/VL6r79n4U9EYUReFQ8iHWRK9hbfRazmWfK3O4QMfA0l73UJdQbPW2mPQmbPW2KIrC4eTD7EnYw56EPYSnhBOTHcP3R77n+yPf42XrxaDAQUxpMYVmrs3q5/clxLXscj3zAN1mqMn8od9g2Otg61onYXy49iQHzqbTN8yTm7oEVHv/yy1Ld6mRwSNZHb2apaeX8kinRzDoDNVuTwghhKhrVU7mIyMjyz336aef8uGHHzJ+/HimT59OcHAwoCb+8+fPZ+nSpcycOZNHH3209iIWALjZGwFIyZb5w6KWxR2Avx6/MJzer5OahLcaV2nveimNRu2tD+wJw15Tk/rd36rJwD+vcfb0Gpa1G8Hys+uIzowu3c2oM9LHrw+Dmw6mh0+PSitNl+jfpD/9m/QH1KJW22K3sSZ6DRvPbSQxL5GFxxey+MRibm91Ow92fBB7G1keUwirlfbM+1T8epMe4NUGEsNh/8/Q66FaD8FiUTgRrxavi03Ps+oY8TnqeVxpmD2o/8d42XmRmJvIqqhVjAsdZ1WbQgghRF2qcjLftGnTMj//+eeffPjhhyxYsIBbb721zGsdOnRg/PjxLFy4kKlTp9K3b99y+4uakeXpRK3Lz4CN78C/X6jLUBmdYejL0PlOtdBVdTl4wYBnsfR5nNVbXufnk7+yVxsL4d8BYKu3ZUDAAIY0HUJf/77Y2Vg3XcTOxo4hTYcwpOkQCosL+TfuXxafWMyGsxuYf2Q+KyNX8nT3pxnedLiMEhLCGlmx6tfKeuY1Guh2Nyz/P3VaTs8H1edq0dm0XPLMasHXuAzrkvmSYfY+9pXclLiIjdaGW1veysd7P2bBkQWMDRkr/38IIYRodKyeWPree+/RtWvXcon8xW655Ra6du3K+++/b20zohJuJcl8riTzooaSTsDyp+D9VrD9MzWRbzMRHtmpFrGzJpFHnQu/6dwmJq+8nafPLGWv0QaNotAzL483MwrY0Pt93un/DsOChlmdyF/KoDPQL6Afnw76lNmDZxPgEEBiXiJPb3ya+9fcX9ozJ4Sohiv1zAO0nwIGB0g5CZGbaj2E4+d75QFi0/OtOkZ1htkD3BR2E0adkaOpR9mbuNeqNoUQQoi6ZHUyf/DgQcLCwq64XVhYGAcPHrS2GVGJ0mRehtkLaygKnFoHP9wIs7vBrjlgzgHPVnDbYrh53uU/uF/B7vjdTF81nYfXPczxtOM42DjwYIcHWTPyZ+bgy9jUBOx+vAnC/6i9c7pEv4B+/DH+Dx7s8CAGrYHtcduZ9Ock1kWvq7M2hbjmFJshJ0n93ukyq0YYHdWEHupkmbqLk/n4DCuT+ZyqVbMv4WJyYUzIGAB+PPqjVW0KIYQQdcnqZN5isVSpQv3p06dlndY64CbD7IW1YvbC/LGw4EY4vQ7QQItRcMef8NB2aD7M6kNHZ0bz2D+Pcdffd7EvcR9GnZG72tzFyhtX8lDHh/D2bgd3rYTmI9QK+IvvhA1vg+Xy1eutZdKbeKjjQ/w+/nfaurclszCTJzY8wev/vk5+kXUJgRDXlWw1AUZrA7Zul9+22wz169FlF9amryXHEy7qmbdimL252ExKfgrAFWtyXGxqq6kArDuzjtjs2Gq3K4QQQtQlq5P5Tp06sXPnTv74o/KetSVLlrBjxw46depkbTOiEiXJfIok86Kq0qLh1xkwZyBEbVaXmOvxIDy2D279GUL6Wz3PNaMgg//t/B8Tlkxg/dn16DQ6JjefzIobVzCz60xcTC4XNjY6wC0/Qc/zRbI2vAW/3gmFOTU+xco0dWrK9yO/5662dwGw6Pgibl1+K6fSTtVZm0JcEy4eYn+lKTfebaBJT3Wqzp65tRrGxT3zWflFZBcUVWv/pDx1dIGN1gZXY9Wr7Ye5htHDtwcWxcLCYwur1aYQQghR16xO5p9++mkURWHy5MlMnTqVFStWcPToUY4ePcrKlSu5/fbbmTx5MhqNhqeffro2YxZIATxRDWnR6pz4z7rC4V/V59rfAo/uhpFvg1uwVYctthRzMOkgn+37jNF/jGbB0QUUKUXc4H8Dv4/7nZd6vVT5cFatDka8BeM+U3v8jiyF74ZD+lkrT/LKbHQ2zOwyk6+GfIW7yZ1T6ae4feXtbIvdVmdtCnHVyywpflfFaTc97le/7pxTazfoCoqKiUxWj6XTqjcc46pZ0b5kvryXnVe1C9nd3up2AH49+Su55txq7SuEEELUpSpXs7/UmDFjeP/993nmmWdYuHAhCxeWvWOtKAo6nY7//e9/jBkzpsaBirJKeubTJJkXlUk4Als/gkO/qj1lAMH91SXjfDtYdci8ojzWRq9lc8xmtsduJ70gvfS1Zi7NeLrr0/T27131A3aeBu7NYNHtEH9IHTUwbQn4tLUqvqro7d+b38b9xtObnmZX/C4eXvswr/Z5lbGhY+usTSGuWlUpfnexVuPANQjSomDfggvJfQ1EJOVQZFFwNOnxc7bleEIWsRn5hHk7VvkYJfPlq1r87mL9AvrRxLEJZ7PO8tfpv5jSckq1jyGEEELUBat75gGefPJJ9u7dy913301oaChGoxGj0UhISAh33303u3fv5qmnnqqtWMVFSpL5rIIiCoqKGzga0ahkxsLCqfBFLzi4SE3kQwaoc+LvWGpVIp+Ym8gnez9h6K9DeWHLC6yMXEl6QToONg4MbTqUN/u+yeKxi6uXyJdo2gvu2wDe7dRCWwtvhdzU6h+nGtxt3flyyJeMDBpJkVLEC1te4JtD30h9DyEulXV+7ntly9JdSqeH3o+q32/7TC2gV0Mnzs+Xb+njiK+LCYD4as6bL61kX4358iW0Gm3p3Pkfj/2IRambGh9CCCFEdVndM1+iXbt2zJkzpzZiEdXgZLJBp9VQbFFIyzHj46xr6JBEY3ByDfxxP+SmABpoNRb6Pgn+na063PHU48wPn8/KqJUUWdQ5qv4O/owKHkVf/76082yHjdam5nG7NIE7/4KvB0JaJPx6F0z9TU0M6ohBZ+Dtfm/jbe/NvPB5fLz3Y+Jz4nmu+3PotXXXrhBXldKe+Som8wAdp8L6tyDjDIQvgfY31yiEY+fnyzf3dsRy/n5bdZenq+6ydJcaHzqeT/d9SmRGJNtjt9PHv49VxxFCCCFqU4165kXD0Wo1uNqVFMEraOBoRIMrNsOa/8KPN6mJvE97eOhfmPKDVYn8waSDPLLuEW766yb+iviLIksRnb0689GAj1g+cTmPdX6Mzt6dayeRL2HrCrf8CDZ2ELEB/nm19o5dCa1Gy/91/T+e7fYsGjQsOr6IO1fdSXRmdJ23LcRVobo98wA2ttDjAfX7rR+rS2HWwIn4Cz3zfs5qz3xcdXvmazDMHsDB4MDEZhMBWHB0gVXHEEIIIWqbJPNXMSmCJ1AUSDwG80arH5oBut0LM9aAV8tqH253/G7uXX0vU1dMZeO5jWg1WkYEjeDn0T8zf+R8BjcdjE5bh6NAvNvA+Nnq91s/hsO/111bF7m99e28P+B97G3sOZB0gJv/uplFxxbJsHshSpP5Ks6ZL9FtBtjYQ8IhOLWuRiFc3DPv62ILQFw115pPzE0ErBtmX+K2lrehQcOWmC1EZERYfRwhhBCitlg9lnTQoEFV3laj0bBuXc3ezMV5SSfUJX+cA3C1V3tcJZm/zuSlQ+RGOP2P+kg/oz5vdIbxn0Lr8dU6XJGliLVn1vJD+A8cTD4IgF6jZ0zoGGa0nUGQc1Dtxn8lbW+EuP1qMr/0YfBsoSb5dWxo06G0cW/DS1tfYmf8Tl7f8Trrz67nld6v1CgBEOKqZk3PPICdG3S5E/6drRbiDBtiXfP5ZmLOV65v4eNI8flx9rE1qGZvrSZOTejfpD8bzm7gp6M/8WLPF60+lhBCCFEbrE7mN2zYcMVtNBoNiqJUexkYcRnxB+Hfz1FcAnH3mAdIMn9dUBQ4twt2fKku42a5aI1lnUEtcDfynWotM5dVmMXvJ3/nx6M/EpejfmA3aA1MDJvIXW3vwt/Bv5ZPohoGz4K4gxCxHn66BaYutmqkQXX5OfgxZ9gcfjr6Ex/t/YitsVu5+a+bmT14Nu0829V5+0I0KoW5kJ+hfl/dnnmAXg/Bzq8gajPE7AH/LtU+xImEbAC8nYy42BnwKR1mn1/lzxcWxUJSrrrOvLXD7Evc3up2NpzdwJ+n/+Sxzo/hZHCq0fGEEEKImrA6mV+/fn2Fz1ssFqKjo1m2bBm///47zz//PMOGDbM6QFFWuEcQX/j40Lwggw6aEyzHSZL5a1lRIYT/oSbxsXsvPO/RAkIHqY+gPmCwr/Ihz2Wd48ejP/L7yd/JLVLXTHYzuXFLi1uY3GIy7rbutX0W1afVwU3fwTeDITUCvhkCk76BFiPqvmmNlttb305v/948u+lZjqUeY8bqGbzb7136N+lf5+0L0Whkny9+Z2MHJufq7+8cAO1uhgM/qyNtJn9f7UOUVLJv4aMmzb7O6jD73MJiMvOLcLa9ct2O1PxUipQitBotHrYe1Y7hYt19utPMpRmn0k/xx8k/mN5meo2OJ4QQQtSE1cl8//6X/1B755138sknn/DMM88wefJka5sRl4grTGejrYH9Bh3vZ/0N3EyKJPPXFosFzu6AQ4vVRD7v/DJtOqP6wbjH/eDbvlqHVBSFA0kH+P7I96w7s650aaVQ51DuaHMHo0NGY9QZa/tMasbODWashV/ugOgt8PMtMGQW9HkC6mG0T4hzCPNHzGfmxplsjdnKY+sf46WeL3FT85vqvG0hGoWL15i39m+uz+NqMn/kT0g/q65cUQ3Hz8+Xb+HtAICtQYernQ1puWbiMvKqlMyXFL/zMHnUeKUKjUbD7a1u5+XtL/PT0Z+Y2mqqrH4hhBCiwdRpAbzHHnuMJk2a8PLLL9dlM9eVgU0G4m90I0On41T+FvQUkZotyfw1IS0a1r4MH3eAuSNg97dqIu/oC4NehJlHYMLsKifyiqJwNOUon+z9hAlLJzBt5TTWRK/Boljo49eHr4Z8xR/j/+DGsBsbXyJfwt4dpv0BXe4CFPX388f9YK5e8Str2dnY8emgTxkfOh6LYuGV7a/w2b7PpDCeuD5kxqpfqztf/mJerSDoBkCBAwurvfux+EzgQs88XOidj6vi8nTxuepNidqqfTE6ZDQuRhdic2LZcHZDrRxTCCGEsEad307u0KEDGzdurOtmrhs6rY7b283gf7vfZaG9nhu0B0jNtb6gj2gEEo6oBaIO/QpKsfqcwVFdI779zRDUr1rrrUdkRLDk5BJWR68mJjum9HmD1sCY0DHc3up2wlzDavkk6pDeAGM/UovgrXwWDi5Si/7d+rO6nF0ds9Ha8Fqf1/Cx9+Grg1/x1cGviM6M5uXeL2NvU/XpDUJcdS7uma+JTrer8+b3/wj9nqpyL7+iKBf1zDuWPu/nYuJIXCaxVVyerqbL0l3KpDdxc/ObmXNoDguOLmBIU+uK+wkhhBA1VefJfGpqKtnZ2XXdzHVlYvNJzN7zIVEG6Of4D3/nyDzeq4o5DzJiIPU07J4LJ1ZeeC24P3S9C5qPUNdqrqLC4kLWnVnHL8d/YXfC7tLnTToTff37MqTpEPoF9MPR4HiZozRy3e8FjzBYdAec2Q7fjYTbfwPnui/Up9FoeKTTI3jbe/Pmv2+yKmoVx9OO80H/D2jm2qzO2xeiQVhbyf5SrcbC8qcgLRKit6l1PqogKbuAtFwzGg2EnR9mD5QWwYuv4vJ0JcvS1aSS/aWmtJjC3MNz2ZOwh2Opx2jpVvcFOoUQQohL1Wkyv2nTJjZv3kyLFi3qspnrjr2NPTc2Gcz3Z/7mmEss+UkZDR2SuJy8NNj1DRxbrs4ZzU2+ZAON+mG375Pg37nKh80157IrfhebYzazOmo1aQVpgFrArV9AP8aHjqePfx9s9VW/KdDohQyAu1fCgkmQdBS+HQbTfleXr6sHNze/mWYuzXhqw1NEZkRy24rbmNVrFqNDRtdL+0LUq9Ke+Rom8wZ7aDMB9v2g9s5XMZk/Ea92BAS522Oy0ZU+XzLMPraKw+xLlqWrzSUmve29Gdp0KCujVvLDkR94o+8btXZsIYQQoqqsTuZfffXVSl/Lysri6NGj/P3331gsFu655x5rmxGVmNr1SRZE/81OOyM9lVUUW8ai08oSgI1KVjxs/0ztfS+8ZHSKjb1aCKpJD+j9qNrjfAVFliKOpx1nV9wutsZuZU/CHswWc+nrXnZeTAqbxI1hN+JjX8NhsY2ZdxuYsRp+uBFSTsJ3w+G2X6BJ93ppvpNXJ34Z+wvPbn6WHXE7eG7zc+xL3Mez3Z7FRnflYlxCXDVqa5g9qEPt9/0A4UvUZTSNDlfcpXS+vHfZEUV+LiXL01VxmH1u7Q6zLzG19VRWRq3k76i/ebb7s7JMnRBCiHpndTL/8ssvl64jXxmtVsvjjz/OE088YW0zohJ+jv4MtgtkTd4ZcPuXjDwzbvaGhg7r+mWxQOY5SD4BySchdj+E/w7F54sTerWBXg+rxeucA8DkUm7eqEWxEJMVQ7Y5m7yiPPKL8skryiMyM5LdCbvZn7ifHHNOmX38Hfzp69+XfgH96O3X+/qpquwSqCb0P02Gc7tg/lgY8xF0vLVemne3deerIV/xxYEv+OrgVyw6vogTaSf4YMAHNV76SohGI6sWCuCVaNID3ELV6UVHlkKnqVfcpWRZuuY+ZZP50gJ4DTjMHqC9R/vSZepWRa5icgtZuUcIIUT9svqT/3//+180lRSxMRgM+Pv7M2jQIAICAqwOTlzeHZ0fYs3W59jjWEhUzCHcmndp6JCuHxYLxB+A0//A6fUQswfMueW3a9ITbpiJ0mwohYoZG60NWs2FRSTS8tPYHrudrbFb2RqzlZT8lMs262jjSCfvTvTy7UUf/z4EOQVV+nd4zbNzgzuWwq8z1LoDSx6AmN0w/C21aF4d02l1PNLpEdp7tue5TWrv/JRlU/h44Me09Whb5+0LUacUpXZ75jUa6Hgb/POaOtS+Csl8SfG7luWSebVnPjY9D0VRLvt/oKIopQXwfOxqd8SSRqNhYrOJvLv7Xf44+Yck80IIIeqdRpE1lgDIzMzE2dmZjIwMnJyunqFyE79uzymjwq22HXhh8oKGDufaUlQAiUchLQpyUyA3Vf2aFQtRW8vPfdfaoLiHcNotkO22JqLtXYjVWIjLiSM2O5bcIjXZN+lM2OptMegMJOYmonDhT9CoM+JscMakV7cx6U142XnRxbsLXby7EOYShk6rQ1zEYoGN/4ONb6s/B3SHyfPBya/eQojKiOKx9Y8RmRGJQWtgVu9ZjAsdV2/tC1Hr8jPg7UD1+xfiwGBX82NmxMCHbQAFHtsHbiGVbmqxKLSZ9Td55mLWzuxPM68Lw/ILiopp8eIqAPa9NBTXy4xK23RuEw+vexiTzsTWW7di0NXujb7U/FQG/zKYIqWI38f9fnWtFNIIZRRkEJ4SzpGUI8Rkx3BLi1to4SZ1l4QQ15+q5qZW98yfOXMGBwcH3NzcLrtdWloaWVlZBAYGWtuUuIywgo6cMu5jZc5B/q+4oPGuF97YWYoh8Qic3aEOkY87oCbyF81JL8fgAMH9yAjqzS57J7ZknWZr3Hbic45DIVBJXcL84nzyiy8MDw1zDaOvX1/6+velk1cnmXddXVotDHxeLR74+71wbid81R9ungtBfeslhCDnIH4a9RPPb36eDec28J8t/2HD2Q1MbzOdDp4d6iUGIWpVSa+8ybl2EnlQV54IHaiOaNr/Mwz6T6Wbnk3LJc9cjEGvJci9bPtGvQ4PBwPJ2YXEZuRVmswrisJn+z4D4NaWt9Z6Ig/gZnKjf5P+rDuzjiWnlvB0t6drvY1rXXJeMl8d+IrNMZvLLKcKkGPO4Z1+7zRQZEII0fhZncwHBwdz55138u233152u2eeeYa5c+dSVFRkbVPiMgrsb8PHvIt4Gz3LDnzHpM4PNnRIV4/Y/XBqDZz5F87uhILM8tvYuoJHc7D3VId127mTbLBjr62JPcVZ7Encx4kTc8r1rnf17kpr99b4OfjhZ++Hr4Mv7rbumIvNZebDe9l51WqF5eta8+Fw3wZYNA0SDsP8cTDsNej5UJXXta4JB4MDHw/6mM/3f85XB79iTfQa1kSvoYNnB6a1nsbgwMHXT00DcfVLPqF+rY358hfrOFVN5g/8DAOeV2/GVaBkiH0zTwf0uvLb+DrbkpxdSFx6Pm38nCs8xroz6ziaehQ7vR13tb2r9s7hEhOaTWDdmXUsi1jGE12ewEYrN2SroqC4gB+O/MCcg3NKR64BBDoG4mhwJDwlnNS81AaMUAghGj+rP1kqinLZ4neXbivqhsnJj57RjizxyGPe0R+Y2On+MnOyRQVi9sL6N9VE/mIGR2jSDfw6g19HMj1COWrO5ET6SSIyIohIjyAieT/pBenlDhnkFERf/7708e9DV++umPSm+jkXUZZbCMxYA389Dod+gb9fgHO7YdynVaqeXVNajZZHOj3CsKBh/HDkB5ZHLOdA0gEObDyAv4M/T3d9mkGBg67fOgfi6rH/J/Vr6ODaPW7LMWpvf8ZZiNqkLjdZgcrmy5fwdTZxKCaj0or2xZZiZu+fDcC01tNwNbnWPPZK9PXvi4etB8l5yWw6t4nBgbX8O7vGKIrC31F/8+GeD4nNUYsstnVvy4MdH6SjV0ecDE5sPreZh9Y9REahLL0rhBCXU+fdRFlZWRgMUmW9rrg5GDiT2h9H1xVEkcXGsxsYGDioocNqnOIPqUn88RXqzxodtBgJwf0gsCcRRls2x20nPDmc8KNfcibrTIWH0aAhzDWsdB57F+8uUsG8MTHYwY1fQ0BXNZkP/12dMjFlAXg0q5cQmrs257U+r/F458dZdHwRi44tIiY7hic2PEFf/7680P0Fmjg1qZdYhKi2zFg4oc5Jp8udtXtsGxO0vQl2fwv7fqw8ma+kkn2JkiJ4lVW0XxW1ilPpp3A0OHJHmztqHvdl6LV6xoaMZW74XJacXCLJ/GXkmHN4ccuLrD2zFlCXC3yiyxOMCh5VpiPCxegCqHPohRBCVK7OknmLxUJ4eDj//POPzJevQ+72BuYX92RG1m/Mc7Fn7t7PJJm/1LndsOVDOLZM/VmjhXaTUfo9zTGNmTXRa1i74yUiMyLL7erv4E9Lt5aEuoQS4hxCiHMIQc5B2Opt6/kkRLVoNNDjfvBpD4unQ9JR+KofdLgFukwH3/qZx+5h68HDHR/m7rZ3M+fgHOaGz2VLzBYmLJ3AjHYzuLvt3TKKQzQ++xaAYoGmfcCzee0fv+NUNZk/+ifkv6f21F+ipGe+RWXJvEvly9OZLWY+3/85AHe1uate1n+f0GwCc8PnsjlmM8l5yfVzgzfzfDFW/87gHlr37dXQ2cyzPLb+MU6ln8JGa8N97e9jepvpFb6fOhvVfxOSzAshxOVVK5nX6cpW0Z4/fz7z58+/4n4zZsyoXlSiylztDORiolVha2yUKPZlnGR/4n46enVs6NAalqKo8zK3fAhRm88/qYE2E2HA80QZDDy96WmOpR4r3UWv1dPTtyddvLvQ2r01rd1a42JyaZDwRS1p2gvu3wSL74Iz29QEYve3ajLf+Q5oNxlMdf9B31Zvy2OdH2Nc6Dje3PEm2+O288WBL5gXPo9uPt3o69+Xvn59pbdeNDxLMez9Xv2+tnvlS/h3Bs+WkHQMwv8o105BUTGRyTkAtPC+fM98bHr5YfZ/nf6LM1lncDW6MrXVlZfAqw0hLiG092zPwaSD/HX6r7qbo590Qr0xfWy5uhQngH9XuHdd3bRXS7bFbuPpjU+TWZiJp60nHw788LLFQUuS+dyiXMzFZikMK4QQlahWMn/x3HeNRnPZufA2NjYEBAQwadIkXnnlFesjFJfl5qBOYdiuGcyY7E/4w9GBuYe/4+NBnzRwZA0oJxl+mnLhg45WD+2nQJ/HwbMFm85t4rk1z5FlzsKkM9HXvy+Dmw6mf0B/HA0Vf3AUVzFHH7hzuTo/d8989YNw3AFY/n/wzxvQ/1noene9rE0f5BzEV0O/YnX0aj7c8yEx2TFsOreJTec2qa87BXFT85u4MexG+bcoGsbpf9T57CYXaFVHyytqNGrv/JqX1KH2lyTzEUk5FFkUHE360qT9Un6V9MwXFhfy5YEvAZjRbgZ2NrVUib8KJjabyMGkgyw5tYQ729xZu7UxMuNg6cNwuoKkPTWi9tqpA9+Hf8/7e97Holho79GeDwd+iJed12X3cbBxQIMGBYWMwgyZyiaEEJWoVqU0i8VS+lAUhTvvvLPMcxc/CgoKOH36NO+8847Vc+Znz55NUFAQJpOJHj16sHPnzkq3nTdvHhqNpszDZLr2h6+6n1+S55+CNtxZoI6cWH92A1EZUQ0XVEMqKlSrmcfsBhs7tZL54wdgwucoHs355tA3PLLuEbLMWXTy6sTKSSv5cOCHjAkZI8nTtUyrVefm3jwXZh6D4W+CWyjkpcKqZ+HzHhC+RB3RUcc0Gg3Dg4az8saV/Dr2V57s8iTdfLqh1+iJyozivd3vMfTXofxv5/84l3Wu3P5SUFTUqT3z1K8db1Pnt9eV9lPUuiXndqq9zRc5cX6+fAtvx0oT4pIkPz4jn9zCPHbF7+KLA18w4+8ZxOXE4WXrxZQWU+ou/gqMCBqBSWciIiOCA0kHau/AR5bCF73URF6rV4sSjvkQ7j8/6iwvTR1R0Qj9HfU37+5+F4tiYUKzCXw34rsrJvIAOq0OJ6M6akqG2gshROWsnjM/a9YsOnXqVJuxlLFo0SJmzpzJl19+SY8ePfjoo48YPnw4x48fx8ur4jcCJycnjh8/Xvrz9VAx2u18Mp+UW0xwxwkMiP6DDfZ2zD8yn1m9ZjVwdPVMUWDFU+pwaqMT3LMWPFsAkGvO5b/b/svfUX8DcFPzm3ih+wsydO96ZO8OvR6G7vfDvu9h/Vtqz9bi6eoce4/mYGOr3gyysVWXJHQOAOcm6lcHbyjMhtzU848U0BvBv0u1KuZrNBpauLWghVsL7m57NznmHFZFruKHIz9wOuM0C44u4KdjP9HEsUnpcob5RflYFAttPdqqQ/P9+9LKvVWjXcHColgITw5nS+wWknOTCXIOKq094WPvQ5FSRGJuIrHZscTlxJFVmIW3nTe+Dr742fvhYnS5Lv4fbzQy4+D4SvX7ztPrti1HbwgbqhbaO/ATDHm59KVjV5gvb1EspJojMXisR2d/nD4LX6BIubD8rQYNT3R5ot7rUTgYHBgWNIw/T//JH6f+qPl0t4IsWPkc7F+g/uzbAW785kIdg2Lz+Q0VyEtX/29rROKy43hluzoy8642d/Fklyer9ffsbHAmoyCjwhVkhBBCqGqUzNelDz74gHvvvZe77lLnnX355ZcsX76c7777jueee67CfTQaDT4+PnUaV2NTkswXFlvIbTmJOw9+zwZ7O34/sQRj1iieGNgZk43uCke5RuycA3vnAxqY9G1pIp9jzuHBtQ+yL3Efeo2e53s8z+QWkxs2VtHwdHp1eH27m2HbZ7DtE4g/qD6sodGBTzsI7AWBPcDWrezrBnvwaqV+rYC9jT2Tmk/ixrAb2Ra7je+PfM+22G1EZ0aX23Z/0n72J+3ns/2f4WZyo6t31wtFGl1CCHIKwqCzbkSUoihkFmYSlxNHbHYsRZYibPW2mPQm7PR2oIEzmWc4nX6aiIwIIjMiMVvM+Nr74ufgh6+9Ly5GF/Yl7mN77HbSCtIqbMdWb0tBcQEWxVJpLLZ6W1yMLpj0JjUGnQk7GztcjC64mlxxNbqWLjmWlp9GWkEaaflpZJuzaebSjC7eXejo2REHQ9mbLPlF+STlJeFt523176mxSs5L5kjKEVq7t67+0OT9C0ApVv8Ne7WsmwAv1nHq+WR+IQx6CbTqe9WJS5J5RVGIzYllT8IedsTtYGvMVlLyUzB6qocpUsDT1pOu3l3p4t2F7r7dCXYOrvv4KzApbBJ/nv6TlZErebrr0+X+7VVZ9HZY8iCkRQIa6PsEDHih7HQgnY1aPDA/Q72p2IiS+WJLMc9tfo6swizae7Tn0c6PVvvGnLPRGbKkZ14IIS6nzpems0ZhYSF79uzh+eefL31Oq9UyZMgQtm/fXul+2dnZNG3aFIvFQufOnXnzzTdp06ZNhdsWFBRQUFBQ+nNmZmbtnUA9sjPoMdloyTdbSHZuR3OdF+3zCzhogu8O/cCaQzm8c1N7ujR1u/LBrmYRG2DV+Zs8Q1+B5sMAtUf+obUPsS9xH44GRz4d9CldvLs0XJyi8TE6wsDnoetdcHKN2utuzoXCXPVrTjJknFMfmTFqsgNqz72du9pzn5uqzjOO268+dnxRcVsaLbiHqT1svh3UiuElx844CwWZaDxb0se3I32ajCe69QyStVpMNrbY6m2x1ZkwW8zsSNjJlnNb+DfuX1LzU1kdvRouyvk1aHA1uZYmvW4mN2z1tmrvfnF+aU//pYl0QXEBcTlx5Jhzqv1rrOimA6hzX3v59aKpU1OiM6M5nX6aM5lnyCtSC5fZaG3wtffF18EXJ4MTCTkJxObEkpyXTF5RXul21bXh7Aa+OfQNWo2Wlm4t8XfwJy47jticWFLzUwH1ZkF3n+709e9LH/8+NHGsvwKEueZcUvNTy9yEMFvM+Nj74Gfvh6+Db2mVb7PFTHp+Oqn5qeQV5eFkdMLV6Iqz0RmtRktcdhzrzqxjTfQa9iXuQ0FBg4ZOXp0Y0nQIQwKH4OvgW9p2kaWIwuJCbPW2FxIsiwX21HHhu0s1H6He9MqKU+fqhw0l15xLePJJdPbxxBTH8dzmU+xJ2EN8TnyZXe30dpAfRmpSMC8PncBtnTs3ilEcnbw6EeIcQkRGBMsjljOlZTWH+hfmwj+vwb9fAIo6ImjilxDUt+Lt7dwvJPONyLeHv2Vv4l7s9Ha8fcPb2GirPwpOKtoLIcSVVTmZHzRoEBqNhvnz5xMQEMCgQVVf/kyj0bBuXdUrrSYnJ1NcXIy3t3eZ5729vTl27FiF+7Ro0YLvvvuO9u3bk5GRwXvvvUfv3r0JDw8nICCg3PZvvfXWNVOYz93eSEx6Hvcv2MvIzG7cxUqeNHlidN3B6ZODuOnL7UzvFcQzI1pga6MjJaeQkwnZnErMIt9swdXegLu9ATd7A+4OBvxdbBvFh6IqSzkNv0xXk6z2t0Dvx4Dzify6h9ibuBdHG0e+Hvo1bT3aNnCwotFy9IHO0y6/jaVY/dBscFDXs79Yxjk486/6iNkDRZcsmZWbAtkJkHxcfRz6peI20qJK1/huev5RhkZLoEcLbvbtgDlkGvtt7Qm35BKRG09EZiQR6RFkmbNIzU9Vk1YrPwe7mdzwtffFqDOWJtX5xfkUWYpo4tiEEOcQgp2DCXEOwaQ3EZsdS2xOLHHZcSTnJdPSrSV9/PvQ3rN9uQ/yZouZ2OxY7PR2uNu6VzhNoKC4gPiceLIKs0rbzyvKI9ecS3pBOmn5aWoyfL7n39Wo3rRwNbli1BkJTwlnT8IeYrJjOJJyhCMpR8ocX6/Rk1eUx8ZzG9l4biOg9u5e2lPv7+BPF+8udPHuQnvP9tjqbbEoFuJz4ktHJthobWjt3prmrs3LDO0uLC7kZNpJwlPCicyILJ1OEJsTW6UExcXoQrFSTFZhVoWvazVanAxO5YYh+zv4E5Mdw97EvexN3Ms7u97Bw9aDguIC8ovyMVvU4dnuJnfaeLShtXtr2hSa8ciLJ9LVg4jCGCL+eZzozGgcDA5llub0svMiMTexdORGXE4czkbn0t9RdUYDKDobIluPYsvJpWz992WO7HlVPRdPsAN+On1hW71GT2uP1nTx7kJfv7508urEYz8fZGV6POZ8t0bznqXRaLip+U28s+sdFp9YzOQWk6seW/R2tchd6vkT73Q7DHsDbF0q38fOXZ0m1IiS+QNJB0qXBnyx54tWr9JRksxnFl6dnS1CCFEfqpzMb9iwAY1GQ25ubunPVVUfb7K9evWiV69epT/37t2bVq1a8dVXX/Haa6+V2/75559n5syZpT9nZmbSpMnVuSyUm72BmPQ8jsVnYdH35ZHc3/EuKiJBn0vf9glsORDAvG1RLDsYS5FFIT3XfMXjdQ9yo3uwGz1C3Gjp44RO2zg+KJWTlw4/3wL56eryPGM/Bo2GvKI8HvnnEfYk7MHBxoGvhn4libyoOa0OHCop3uQcAO1uUh+VyYqHuINq7338IXWuvXPA+UegeoMgIVytth+7X126S7mksJVigaSjkHQUm4ML6QZ0A7XX3+SCYudGiq0nqRSRVpRHmqWANEsheRoNJpMLtrZu2Np5YnTwQufoB07+4OAJGi16rR4fex987X0rXPu5WhRFHdUQd0i90aFYoEl3cPLDRmtDU6dytynKMOqMV9ymKuJz4tmTsIf0gvTSc/Oz98PJ6MSJtBNsidnC1pit7E/cT1JeUrn9Y7Jj2BmvFl/Va/UEOgYSlxNX4YgBnUZHM5dmBDsHE50Zzcn0kxRZisptd/E5XjxdQK/VE5cTR1x2HNnm7DJJugYNLkYX7GzsyCzIJMuchUWxkF6QXtoLP7TpUAYHDsbXwbdcb31yXnK59lPyU8qspoD/+WlqR+aV2a4qxdx+PvYzoK7I0MmrE81dmxPiot4A8LbzLm2vZHrG8dTjbI/dTmxOLLi7giUTzg+WU4pNaC1u9A9uTkv3luqNFI/25SrT+zpXvtZ8QxoXOo6P9nzE8bTjhKeEV+29Z/1bsPF/gAKOfjDuE7WmwJWUTOdpJMl8dmE2z256lmKlmFHBoxgTMsbqYzkb1GRe5swLIUTlqpzMr1+/HoDAwMAyP9cFDw8PdDodCQkJZZ5PSEio8px4GxsbOnXqxKlTpyp83Wg0YjQaaxxrYxDiac+hmAy6NnXlfzf1R/fHD4zPiuBrV2fsPfYy/+4beeH3Q8ScX49Xo4FANzvCvBywN+pJzSksfSRnF5CaU8iq8HhWhavDGgNcbZl3V3eaeVk596+uFJvhlzsg+YT64eeWH8HGhLnYzKPrHmVX/C7sbez5cuiXtPNs19DRCqH2/jv6lE4DqdDFw2nN+eqw/4uZcyHhiJrwxx1QbwxkxqjJcl4qmrxUPICK+0djK25TZwT3ZuDSBIoLwZyntmPOU1+zc1N7AO091GkJ+Zlq8pCbrE4xMF+S2FqK1FEIl45OAHAJPF9XoCd4tlJvZDj6qjUM6oCPvQ+jQ0ZX+FpLt5a0dGvJPe3uIaswi+jM6DKrBRQrxZxIO8GehD3sTthNYm4iERnqMmB6rZ6mjk0JcQkhvyif8JRwUvNTOZ52nONpFwqxuhhdaOPehjDXMPwc/EqH0Pva+6rLb1VyszuzMJP4nHj0Wj2uRlecDE7otBfqn5iLzeoIhYI03E3uuNuWnS/t6+DL7a1v5/bmk0lJO01CUji2GbHYZpzBlBqNLukEp3NjOWIwEG40cMRoIE2nI8izHSEebQh1CSXIKYjMwkwiMiI4nX6ayIxIkvOS8bLzKnMeCbkJ7I7fzYm0E0RlRhGVGVUmFnsbe7QabYUjDGy0NnQ1K/RNT6Jb1wf51+ZGXlkawQ1hHnw6uMdlr21JRfvGlsw7G50ZFjSMZRHLWHxi8ZWT+SN/wsa31e+r0ht/Mbvz172RJPNv7HiDmOwY/B38ebHnizXqzJFh9kIIcWVV/vTUv3//y/5cmwwGA126dGHdunVMmDABUJfFW7duHY888kiVjlFcXMyhQ4cYNWpUncXZWLw2oS1TujWhZ7A7Wq0G2k9hwtoX+drVme2x23m5VxGrn+zHrqhUPB2NhHo6VFoUr7DIwqGYdP6NSGVHZCp7olI5l5bHtG93sPiBXgS41t+avZelKOo64ZEbwcYeblukJknAbyd/Y0f8Duz0dnw55Es6eHZo4GCFsJKNqeLlwVwCocWICz8Xm9XlqXJTzj9SQWcoW5XfYoaMmAvz89PPqFNUUk5BcQEkhquPWqVR/y6dA9SbBPGH1HbTz8DBRRdtplVvyDkHqDcUSkYrOJ2fIlV6XinqDYKSQoNuIerdyVrgaHCsMOnq6NWRyS0moygKMdkxRGVG4e/gT4BjQJnpA4qikJCbQHhKOFEZUQQ6BdLarRV+Ons0mTFq7OY8yEiB5HPqzZJL6WzUc3YOwMk5ACfX5mpF8/hDF27epJ8FR29snAPwdG6Cp3MAWI6Xrb2QEXPhRktBJu5ARaXROqKho0tz9cZKYC/1RpKj9UVkMwoy2J+4nwNJB0p74M9mnS2twaDVaAlwCCjtse/i3YWu3l2x2zNfrXlybA2LfCYA0LKSSvYX83VR/zaiU3I4m3rh92lr0OHh0LA3629qfhPLIpZduRBeZiz8pU4No88Tas2X6rA73zOfl2p1rLVlWcQylkUsQ6vR8vYNb9d4uVdJ5oUQ4soaZQE8gJkzZzJ9+nS6du1K9+7d+eijj8jJySmtbn/HHXfg7+/PW2+9BcCrr75Kz549adasGenp6bz77rtER0dzzz33NORp1Asnkw29Qy/qh2tzI01WPU+3vHx22ZpYenopD3R4gAEtrry2q0GvpUtTN7o0dePhgZCSXcCUr//lVGI2t3+zg18e6IWXY/0u91Oh7Z+ples1WrjpO/BtD6hVqr8++DUAT3R5ouZLAwlxNdDZqMP/K5sCUMKvguVELcWQHg3JJ9XEwsa27E2Aonw1KcxJVhPSgkwwuZwv/ne+AKCN3SVJtUZdeszRr2z17YIsOLdLrStwdodaHyAjRr3RkHlOfZz9t+rnbe+pJqKOfmUT/vwMdXnKkhEFdu7q76Z0SkOAOr1AX0nCZ85X6x6c26Vu4xGGxqM5AU4BBDgGqDcT8zMuJNDZCWhyU/A5/yAn6cKNk0rmu1eJ0Vn9faNccdPL0mjBwQc8wtSlFz1bqN/7dgBb15od+yLORmf6N+lP/yYXbvabi81EZ0ZjwUJTp6YYdRX8zttNhtUvQew+csyHAEeae1chmT8/zP7AuQxueKfsaMFbujVh1tg22BoaZjWXzl6dSwvhrYhcUfEKKhaLWrE+L029FgP/U/2GSnvmGzaZP5t1ltf/fR2ABzo8UCvvvaXJfKEk80IIUZlGm8xPmTKFpKQk/vvf/xIfH0/Hjh1ZtWpVaVG8M2fOoNVeKJqUlpbGvffeS3x8PK6urnTp0oVt27bRunXrhjqFhuPoDU26MzH1ELtsTSw5tYT72t9n1VrU7g5GFszowc1fbSMqJZdp3+xk0f09cbFrwOWcji5TP/iBOhzxoh7KRccXkZSXhJ+9H5PCJjVQgEJcRbQ6tYfbLaTu2zI6Qugg9VHCUgzZiRf1Kp8r28us1V1048BdTUxj9qiPnCQ4+pf18Th4X5TgN1FvSJzdCbH71JEEl7KxU6cEZCdWL0m3cwd7L7UmQslNEr1RXc7wYua8C+dekKE+QO2tL1kBwS1YPe/0sxd+X1qdGn/JeTj7qzc6Sn5nJhfQVv///9pgo7OhmWuzy29k7w5hw+D4clom/w3cREsfpyseu42fEx2auHAyoey1yC0sZuGus+yJTuOz2zpXul59Xbq0EN7NzW8uP+R8xxfqSix6W3X9eL0V76uNYJh9kaWI5zY/R445h85enbm33b21ctySOfPSMy+EEJXTKBdPELyMV1991fpGNBpeeuklq/evD5mZmTg7O5ORkYGT05U/RDR6Wz4ib93LDApqSjYWvhn2DT18Lz//8HLOpORy05fbSMwqoEMTF368pwcOxga4FxR/GL4dqg5R7ToDRr9f2iOYa85lxG8jSCtI49XerzIxbGL9xyeEqB/mfDXpPvuvOoff3uOi5NVZHQVwcW99VtxF0wzOwZWWvHPwhiY9AEUdtZByWh1BcDE7dzWBdvA5374b2J2Pw8lPnQ7h5F9+5YOqyM9QR0rYeagFCq91h3+DX+/mjMWT/uaPOPLKSKt71beeSuaJRftJyirAqNcya2wbbu3epN4r3qfnpzN48WAKLYX8PPrnstM44g/DnIHqTaPRH0C3GdY1cnQZLJoKAd3gnrW1E3g1fbbvM746+BWONo78Ou5X/Bz8auW4h5IOcduK2/C192X1Tatr5ZhCCHG1qGpuWuVs7OWXX0aj0VDF3B+gdPurIZm/5rQcg+3aWYzMymKxoz1/nPqjRsl8oLsdC+7pwZSvtnPgbDqP/7yPb6Z3rdcPR3nZmZgX3I6TOZdU7z64jXynzNDeH4/+SFpBGoGOgYwNHVtvcQkhGoCNCZr2Uh/VpSjqsOSMM2VHApjzIKCrOnTfNbjs1IHiInU6QmasOq/c2iS9qkzO6uN60XwkxXo7AouSGOkSU6Ph8X2aebDy8Rv4v18OsPFEEi/8cYhV4fHc1CWAQS29KrwRXfLZpjbf01xMLgwNGsryiOX8euLXC8m8OR9+v1dN5JuPhK53W99IA/fM747fzZxDcwB4qddLtZbIg8yZF0KIqqhyMj9r1qy6jEPUNo9m4NGCGzMjWexoz9rotWT2yMTJYP2og+bejsy/uzs3fbmddccSWbTrLLd0D6zFoCt2KjGLH3ecoc2el7iJSBIUF+7PfoAlF1W/zizMZG74XAAe6vgQem2jnUEihGhoGo06tNveveI6AhXR6cE9VH2I2mewI9JjAM3iVzDJsB24v0aH83AwMvfObnyzJYJ3Vh1n04kkNp1IwqDX0i/Mk8GtvMjKN3MyIZtTSdmcSsjGydaG1ye0ZWDLK9eXqaqbm9/M8ojlrIhcwTPdnlGX11v1LCQeUadejP+sZkUcGzCZzyzM5Pktz2NRLIwPHc/I4JG1evySZD63KBdzsRkbnc0V9hBCiOuPJPPXspajaLPlQ5ppbDlVnMfKiJVMaTmlRodsH+DC08Na8MaKo7y27Ah9mnnQxK3ueqdeW3aEb7dEMkr7L7MM67Cg4anih9mfouN0UjahnmqF4Pnh88kqzKKZSzNGBI24wlGFgKSsAvRaDa72Fc9TVRSFhMwCtFrwdDBW2GOnKAopOYVoNRrcKjmOEKJqNhn704wV9MzdqI6EqOFyhVqthvv6hTKopRe/741h5eF4IpNzWHs0gbVHE8ptn1VQxF3zdnFP32CeGdESg77mdQY6e3UmwCGAc9nn2HRuEyNS4mDPPEADE79Up2fUREkyn5+hrmpRjwnvJ3s/IT4nniaOTXi+x/O1fnwHGwc0aFBQyCjMwMO2hr8rIYS4Bkn35bWs5Rg0Wz5kYloK77rY8cepP2qczAPc3TeY1Ufi2RWVxlOLD/DzvT3VJfFqWWJWPt9tjSRAk8R7pm/BApq+MyG6P5xMZs2RBEL7O5Can8qCIwsAeLjjw2XWYhbXPotFIddcXO55OxtduX+XUck5rDwcz8rDcRw8pw7d9HAw0MzLgWZeDng7mohOzeVUYjanErPJLigCwMmkJ8zbkTAvBwJcbYlJz+dUYhanErNJyzWj0UCXQFdGtvNlRFsf/F1s6/7EhbjGLMtpxQTFATdzKkRthtCBtXLcZl6OPDOiJU8Pb8HxhCxWHorn34gUPByNhHk5EOblSIinPYt2nWXetii+2RLJzqhUPrmlE0Ee9jVqW6PRMDJ4JHMOzWHlkZ8YsWeF+sLgl6DZ4JqfnK0LoAEUtSr+lVa0qCWHkg7xy/FfAHi518vY29Ts91QRnVaHk9GJjIIMMgokmRdCiIrUWjKvKAopKSkoioK7u3uZSvOigfh1BgcfxmQk8qGrI+Ep4RxPPU4LtxY1OqxOq+G9mzsw8uPN7IhMZd62KO7uG1xLQV/wz9FEdEoR3zh8gV1RDgR0QzPweYbtjGHzyWTWHknggf6hzDk4h9yiXFq5tWJwYC18OBKNWrFF4WhcJv9GpLAzMpWdUamk55rLbafTanC1s8HN3oCbvYH0XDPH4i9UvdZo1KnTydmFJGen8m9E+aWddFq17kdmfhF7otPYE51WbpuS4+yOTmN3dBqvLTtChyYudG3qStj5mwRhXo4428kQUSEqY7EoHEvMY6WlB1P16+DQr7WWzJfQaDS09HGqtFL+y+Pa0DvUnWd+O8jBcxmM+XQLH07pyNDW3jVqd0TwCOYcmsPmpL1kKUU4thoHfWfW6JiltDp1ecG8VHWofT0k80WWIl7991UUFMaFjqO7b/c6a8vZ4FyazAshhCivxsn8mjVreP/999myZQt5eWp1YJPJxA033MCTTz7J8OHDaxyksJJWCy1G4rZnLgNt3FlTmMiSU0t4tvuzNT50U3d7XhjViheXHOZ/q47Rr7knzbwcaiHoC9YcSeAJ/W+0LDqmrhk96VvQ2TC4lTcvLQ1nz5k0lp5YzYKjaq/8Y50fq/dqxaL2xWXksSMilR2RarKenF12ibCComLyzZYrHqfYopxP1C/sr9Nq6B3qzoi2Pgxr7YOdQUdEUg4nE7M4mZhNQmY+Td3sCfNWk/Agd3ssikJkcg4nE7M5lZDFubQ8/FxsS7cJ9XQgLbeQvw/Hs+JwPLuiUjlwNp0DZ9PLxOPvYstNXQK4tXsgPs6mWvldCXGtOJeWR25hMSv0fZjKOnXJwTEfqEv41aNhbXxo6+/MEwv3szMqlUd+2sviB3rRPsDF6mOGOTQlVNFzWlPEPz6hjJ/wec3myV/Kzu18Ml8/a83/dPQnjqUew8ngxMwutXRTohLORmfIkiJ4QghRmRol808//TQffPBBuQr3eXl5rF69mjVr1vDEE0/w/vvv1yhIUQMtR8OeuUxIimWNs55lEct4ssuTGHQ1n987tUcgf4fHs/lkMv+3+AC/PdALva52RmTkFhZhe3oFj+iXqk+M/QhcmwLg52JLGz8njiaf4tUdXwBwa8tb6evft1baFrVPURRiM/I5maAOTT+ZkM3ppGxyCssOj8/MMxOTfoUlwwBHo56uQa70CHGne7AbLX0c0V704diiKGTmFZGaU0hqTiEpOQVoNBpuaOZRbo58uwBn2gVcvmp4K18nWvlWXjzS19mWO/sEc2efYBKz8tlwLIlj8VnnC2tlEZuRT0x6Hh+vO8ln608xuKUXt/dsSodLEgQbvQY7g8x+EtefY/GZAKR7dIViP8iKhZNroNWYeo/Fz8WWn+7twX0/7OGfY4nc9/0e/ny0D16O1t2E06z+DyPSk5nt6sLKJm0Yb6zlde/t3CHlVL0UwYvPieez/Z8BMLPLTNxt3eu0PSej+v9uekF6nbYjhBBXK6s/NS5YsID3338fW1tbHnroIaZPn05wsDrUOioqivnz5/P555/z0Ucf0alTJ26//fZaC1pUQ3A/MDjQOzUWL6/2JBaks+HsBoYFDavxoTUaDe/c1J5hH27iwNl0nvv9EO9Mal8r8+f379rCO9rZACjd70fTdlKZ1/u3dCQy6gcKLXl09urM092ernGb4vLMxRaiU3I4k5qLl6OJUE+HSpePslgUTiRmsTMy9XwveyrJ2QVVaken1dDWz4nuwW70CHYnyMO+TCeWTqOhiZsduiv8O7Mz6BukB9zL0cTkbk3KPJeVb+afY4n8uOMMOyNTWX0kgdVHyhfgAmjh7UiPELfS8/d0rN+eSSEawokEdQpMc19ncLkRtn8GhxY3SDIPoNdp+eiWjkycvZXTSTk88MMefr6vJ0Z9NWuyHP4dds1hpF7PbFcX/k0+SFp+Gq4m19oLth4r2r+9823yivLo5NWJiWET67w9F6MLoFbOF0IIUZ7Vyfynn36KTqdj1apV3HDDDWVea9OmDe+88w7jxo1jwIABfPbZZ5LMNxS9EcKGog//g/E2PswpSOf3U7/XSjIPao/k+zd34IEFe/h1zzkcjHpmjW1ds+Hu2Um03HA/dpoCTjt2I3T4m2VetigWjpi/QmdMQjE782afd7HRynzk2pRvLubguQx2RqYQHpvJycRsopJzKLJcGIWj0UATVzuaeTngbm8gLbeQlPM94clZBeV63fVaDcEeJUPYHWnm5YDrJfPIDTotbfydK1wH+mrmaLJhfEd/xnf052SCutTi73vPkZlfVG7b4wlZHE/I4vvt0QCEeNrT43xi3yPEDV9nKa4nrh0Wi0J6npkD5wtStvBxhLCb1GT+xCooyILa7smuIieTDd9M78b4z7aw90w6Ly05zP8mta/6+1taNPz1BABNez1O67xwjqQcYU30Gia3mFx7gdq5qV/rOJnfcHYD686sQ6/R82LPF9Fq6r42krNB1poXQojLsfoT8+HDh+nbt2+5RP5iJa/v2rXL2mZEbWgxGsL/YEJCFHPsYVvMNuJz4vGx96mVww9r48O7N3Xg/xYfYN62KJxMemYOs7LIXlEhyi/TcDPHE2nxJnnEV4ResjzRVwe/Yk/yFlD05J67nWMxCv6Vj4AWFylZSq1kmHtJtfYSWflmdkelse9sOoVF5eel2xt0NHGzIyEzn7RcM2dSczmTmlthW7Y2OroGudI9yI0eIe60D3DGZCMrDYR5O/LyuDbMGtuaYkvZKUrpeWZ2RaojGXZEpnIsPpOIpBwiknL4eedZAJq42aqJfbAbPUPcCXC1lVoRolFJzSkkMSuf1OwLN/jUrwXq99mFpVNg0nILufjPoIW3I/iGgHszdej4seXQ4ZYGO5dgD3s+u60zd87dyS+7z9HK14m7+lSh4GuxGX6bAQUZENAdBjzPyKM/ciTlCCsjV9ZyMl/SM193c+ZXRq5k1jZ1ieJpbabR3LV5nbV1sZK15mWYvRBCVMzqZN5kMuHn53fF7fz8/DAYZP3lBhU2FLR6AhNP0KXLSPakhvPn6T+5r/19tdbEpC4B5BQW8d+l4XzyzykcTHru6xdavYMoCqx4Cs2Z7WQqtjyufY7fWl740BSfE8+XB77kt5O/AdDZbgYb85uw9mgCg1vVrNrw1chiUdh3Np2/w+OJScsj2MO+dIm1UE8H0vMKS+enn0zMLrOUWlV4OBjoEexOp0CX0mXZfJ1NpYljSnYBJxOzOZmQRUaeGVd7A+72BtwdjLjZGwh0s8OmlmooXIs0Gg16Xdkk3MPByMh2voxs5wtAem4hu6LS2BmZwo7IVA7HZHA2NY+zqef4dc85AHydTTTzcihTM0CjgQBXW5p5OpReO09H4xWTfotFITPfXJqApeYUEuRur/aWCnEFkck5vLH8aIVruF+Jo0lPa18neoa4q/+A294EG99Wq9o3YDIP0K+5Jy+MasXry4/y2rIjZOYV8eCA0MuvQ7/hLTi3C4zOMOkb0NkwPGg47+95nz0Je0jIScDbvpbet+pwmH1hcSHv7nqXhccXAtDDpwcPdniw1tupTEkyLz3zQghRMauT+S5dunDw4MErbnfw4EG6du1qbTOiNti6QFBfiNjAjXp39gB/nPyDe9rdU6vD5O7oFURWfhHv/n2cN1ccw9ZGx7ReQVXbOSseVr8Eh35BQcNj5kcJbt8JG52W1PxU5hycw6LjizBb1ET0zjZ30t15Chv37mTt0UTesCh1stZ9Y5ORZyY8NoPV4QmsOhxPfGZ+tY9x8fB4V7uyN9oMeg3t/F3oEeJGiIf9ZZM/dwcj7g5G9cO3qBMudgaGtvYuXRorK9/M7ui087UIUjh4LoO4jHziMq7878BGp7liMl9UbOGSwQIAdA50YWqPpoxu7yujK0Q5GXlmPl13kvnbozAXK2g04GZnKF0WsuThXvK9g7H0e3d7Ay52hvKJcdtJajIfsV7tcS4ZSt5AZvQN5nRSDj/vPMOHa0+w8nAc79zUvuIq9xEbYfMH6vfjPi4t3urr4Esnr07sS9zH6ujVTGs9rXaCq6NkPjY7lqc2PsWh5EMA3NvuXh7u+DA6bf39H1CazBdKMi+EEBWxOpn/z3/+w+DBg3nnnXd45plnKtzm3Xff5ejRo3z66adWByhqSevxELGBIWcO8qajPeeyz7EnYQ/dfLrVajMPD2xGVn4RX248zUtLw4lMzuWFUS0rr3JfVAg7voCN70BhNqDhM+MMNuR35LPW3vx49Ec+2fsJuUXqUO6u3l15vPPjdPTqSGGRBQejnqSsAg6cS6dTYC0WFKpFGblmdkalEpGUjZOtTZkPtZdWLi+yWEjPNZcOSU3JLuRcWp7aw56YRUJm2SJyDkY9g1t50dbPmciUHE4lZHMiMYv0XDM6rYam7naEnV/nPMxb7bG/XOE60bg5mmwY2MKLgS3UtaRzC4vYdyad+EuS+SKLheiUXE4lZnMqMZuolBzMxQpQQaZeUTtGPW4OBpxMNhyNy2TvmXT2nknnteVHGNPeF71WW+bfaMElUzI0gKOtTZmEzc3eUDp6Q33OiJuDAXuDrlamCZQUaDyVmE1Mej4dApzpHOh6XdzkayhJWQX8eSCW2etPkZqjLgE5sIUn/xnduuZLlXo2B++2kHBYHWrfuZYSXytpNBrenNiWniFuvPLXEY7FZzFh9lbu7RfCk0OaX7jJlZMMv98HKND5DmhTtkjciKAR7Evcx6rIVY06md9wdgMvbn2RjIIMnAxOvHXDW/QL6Fdrx6+qkjnzmQVSAE8IISpidTKv0Wh45JFHeP7551m8eDHTpk0rrWYfGRnJggUL2LNnD4899hharZZNmzaV2b9fv/p/U7iutRoPy5/CLu4gI8Jm8NvZNfxx8o9aT+YBnh3RAqNey8frTvLd1khOJmbx6a2dcLmkF5iIjbB8pjovEiCgG+d6vcL7P6SrvYj24by95W0AWru35vFOj9PLr1fpB3+DXkv/Fp4sPxjHmiMJjSKZLyxSE4rjCVnsjkornfesVC2HqhJvJyN9m3kyqp0PfZp5lOspVRSF9FwzdkZd9Ssvi6uKnUFPn2YeV9wu31xcmmxdjl6rKddLmpiVz+Ld5/hpxxli0vNY8O+ZGsV8KYNeW5rgO5ls0FZzsJCiqEll5CUFGkH9WxnexoeRbX3pFOhy/kZZwYUlC7PLzufON1twtbPBzd6Iu4Mak8mmbEB6rZYgd3tCveyvy2UE4zPyWXU4jhWH49kVlVr6f1szLwdeHN2KAedvNNWK1hPUZP7IkgZP5kH93DO+oz99m3nw8l9H+OtALF9tjOBoXBbf390dUk7DotshOx48WsCI/5U7xrCgYfxv1/84mHyQc1nnCHAMqHlgtZjMFxQX8MHuD/jp2E+A+t77wYAP8Hfwr/GxrSFz5oUQ4vI0yqWLxFeRVqtFo9GUrjF/ac9KZc+XPFdUVL6Kc0PKzMzE2dmZjIwMnJyu0WpqCybBqbUc6HUft8evwqQz8c/kf3A01M182JWH4pj5ywHyzMUEudsx546uhHmfbyv5FHzeEyxmsPeCoa9A+1v4ekskb644RvcwOGf3BlmFWdzW8jae6/5chf+Wlu6P4fGF+2nu7cDqJ/vXyXlUprDIwqGYdP6NSOXQuQxOJmYRnZJbLqEAtSJ5Wz9ncgqKysxHLigqW/Fdo9HgUtJ772DAzd6ozon2dKCZtzof3skklftF/Su2KGw8kcimE8nYGXRl/o3aGXRoLtk2I898UaJ8IWlOK/1eTZ5rk51BRzMvB7wcjeyISCWroG7fZwJcbQnzcsDOoC9zkyAzrwgHk77M8HJ7o77M70ijgebejnQPdqO1r1Plo5fqSUp2AWdSc3GxK7mxokej0ZCcXVA6rUO9OZlVZr8OAc7c1LUJt3RrUvs1MpJPwmddQauHp042+FD7S60Oj+ehH/dSZFHYNjEfv3+egIJMcPCGaUvAu3WF+92z+h52xO3g8c6Pc0+7e2oeSMpp+LQzGBzhhXNWHWLloThOpZ9mQ9qHnEg7AcDtrW7nyS5PYtA1XN2jM5lnGP3HaOz0duyYuqPB4hBCiPpW1dzU6m6Ffv36SQXlq01bNZlvf3IDoQGhnM44XftVdS8ysp0vTd3tuff73USl5DLx8208MqgZ03o2xX7tLDWRD+4HUxaASb37vuZIAlBEusMPZOVn0c6jHU91farSf2sDmnuh02o4kZBNdEoOTd3t6+RcQL1BdTwhi9XhCfwbkcLeM2kVJiMORj2hXg50CHCmR7A73YJd8XKs//XOhahNOq2GQS29GdSy9opN5hYWlalsnplfteKMl3K2tSHM2xFfJ1PpsPqComK2nUph5eE4Vh9JKJ164mp38dxtQ5mpAEYbHeklSyyej6uwuOzfeL65mIikHFJy1Ckw59LyKoyp5JyqwsGop2uQK92C3NRpMd6OBLrZoavjKQIJmfn8HR7PikNx7IxMLVMvwUanwclkQ0oF59ClqSsj2/owoq0PAa52dRegRxh4t4OEQ3BsmTpsvREZ1saHgc3daX/qc/xWLlGfDOwFN88Dx8pXixkVPIodcTv46/RfzGg7o+afpUpuchRmqVPX9NVLvg+cTeexv77D6PsbGq0ZN5Mbr/V5rUGG1V+qpGc+tygXc7EZG53czBZCiItZ3TN/rbkueubzM+DdZlBcyPwRz/Pe8R9p59GOn0b/VKfNpmQX8NCPe9kRqS6bM8j2JN8ps1A0OjQPbQfPFqXbdXtjLTaeyzC4b8HR4MjisYuvOLxv6jf/svVUCs+MaMFDA5rVauyKonA4JpOVh+NYeTieyOScMq+72RvoHuRG1yBXmnur89J9nExyo0uIRqSo2EJOQTGOJn2tzaFPzSksrWVRWGS5UAfA3oCTrZ7sgqIyS7PlFJYdJVBYZOHguQx2RVY8gsCg1xLiYY+fi23Z4nFlCspVre6AoigkZRdwqnRli2wOx2aw/2x6mSlA3k5GsvOLyCksO2KopY8jPYLVJSa7Bbnh6Wis2S+vOja9B/+8BqGDYNof9dduVRQVkDRnEp4JmwGwdL8f7fA34AoJZ1ZhFgN/GUhBcQELRy+kjUebmsVhscBr7qBY4P+OX/ZGwqWKLQpjZ6/mjMNzaLRFtHDqzBfD38PTzrNmMdWSYksxnX7ohILC+snr8bC98rQiIYS4FtR5z7y4CpmcIWwYHFvGmIwMPtLoOZR8iIj0CEJcQuqsWXcHIz/e04Ol+2OZ/c8JHsuaD1pYrAxk9YosMvK2kZJTSFJWAVr7cAzuWwB4vc/rVZqnN7a9H1tPpfDn/thaSeYtFoX959JZeUhN4C/ueTPotfQL86R/C096BrvRzMtBEnchGjm9TouzXe0OAXezN9A92I3uwTUb+l1sUTgal8mOyFQOnksvLVpYUGThWHxWuWHtFfFyNNI92K004W7m6cCppGx2nB8evzMylcSsggr37RTowqi2voxo60MTN7WXvaTGQlpuIX7OtrjaN+Dysm0mqsl8xEbISQH7RrR6xoa38EzYTB4Gni28l0nNnqR/FXqOHQ2ODAocxMrIlSw9vbTmybxWC7ZukJuszpuvRjL/445oTuZsweRURHG+DyNC/ttoEnkAnVaHk9GJjIIMMgoyJJkXQohLSDJ/vWl7IxxbhvvR5fRuewObYjaxPHI5j3Z6tE6b1eu0TOoSwESbf9H+HkEutrybfyNJF61HrNGnYR+4GIA7Wt/BoMBBVTr2yLa+vLT0MMfiszgen2X1mtgJmfl8ufE0Kw+VXfLN1kbHwJaejGjry6CWXjgY5c9GCFE7dFoNbf2daevvXPpcsUUhJi2PU0lZJGYWlKs7kJpTUNrrX1BkITGrgGUH41h2MA4Ag05bbnqAVgNN3e0J9XQgzNuBZp4O9G7mjq+zbbmYTDY6/Fxs8XMp/1q9cw8Fn/YQf1Adat9lekNHpDq3G7Z+DMAfwa/w59GmsOcc/ZtXLREeFzqOlZErWRm5kqe7Pl3z4eN27heS+SpKzMrn3VXHsfHdA4A5owtRKbk1i6MOOBucS5N5IYQQZdU4K8nPz2f37t3ExsaSn1/5Wsd33NG45rpdt5qPABt7SI9mlMt9bIrZxIqIFTzS8ZG672E256Nd9woApgEzedt7CLEZ+aXDR3+L/pRVZ/Jp59GOJzo/UeXDOtvZMKCFF2uOJPDngRie9mlZ7dD+OZbAU4sPls5xtTfoGNzKm1HtfOjf3EuWchNC1BudVkOgux2B7leej55dUMThmAy1SF1kCnui1VoeJhstXZq60iPYne7BbnRs4lJu5YurRpuJajIf/kfjSObNebDkQXVYe/sptOl2Gxzdyt/h8WTmm6tUpLSXby88bT1JyktiU8wmBgcOrllMVlS0f2P5UbKVOBxsz6JBS1FGR6KSG2Eyb3SGLCSZF0KICtQomX/33Xd58803ycy88vqfksw3EgZ7aDESDv/KwKQz2OptOZd9joPJB+ng2aFu2975NWScAUdftL0fYbDhwgdVs8XMMzv/AeDhjg9Xu5diXAc/1hxJYOn+WJ4a1qLKNyYKiyy8s+oY32yJBKC1rxNPDm3ODWHll3wTQojGxsGop2eIOz1D3IEwCossnEvLJcDVrswSg1e1NhNg3SsQuUldx92+gYdar38Dkk+Agw+MeJv2ts4083LgVGI2Kw7GcUv3wCseQqfVMSZkDHPD5/LnqT9rIZk/P92jisn8tlPJLN0fi9FzLwDt3XqwpdixXF2YxsDJqM4VzSiUZF4IIS5ldTL/2Wef8eyzzwLQrl07wsLCcHSsmyXORC1rOwkO/4rdkT8Z2HUcK6JWsiJiRd0m87mpaiEjgEEvgaFsj9P22O2kFaThZnKjh2+Pah9+SCtv7Aw6zqXlsfdMOl2aXnnN+ajkHB79eR+HYtQPCHf2DuL5US1lbXYhxFXLoNcS4unQ0GHULrcQ8O0Icfvh6F/Q9a6Gi+XMDtj2mfr92I/Bzg0NMKlzAP9bdYzf9p6rUjIPMDZ0LHPD57IpZhNp+Wm4mq78vlWp0p751CtuWlBUzItLDwMWnD0PkqfAxLDxbNkKMel55JuLG9XNbBejCyA980IIUZEaJfN6vZ7ffvuNsWPH1mZMoq41GwxGZ8iKY7RDCCuAVVGreLrb0+i1dTQffOtHUJChLjPU4ZZyL6+IXAHAiKARVsVga9AxvI0Pf+yL4a8DsVdM5s+k5DJ+9lYy8sy42Nnw7k0dGNq69pbcEkIIUYvaTFCT+fA/Gi6ZL8xVh9ejQIfboMWI0pcmdvLn3b+PsSsqjajkHII8rrxMaphrGK3cWnE09SgrI1dyW6vbrI+tGsPsP1l3koikHNzcz5KnpOBocGR0s8G8YtpIZn4RUSk5tPRpPKv6OBvUehKSzAshRHlWj8GLioqiX79+kshfjfRGaKVet16xR3E1upKan8qOuB11016xGfafX/5uwHOgLXvHP9ecyz9n1CH2o0JGWd3MuA5+ACw7GEtRcfn130vkm4t58Mc9ZOSZaevvxMrHb5BEXgghGrPWE9SvUZshO6lhYtj4NqSeBkdfGPFWmZd8nE30aaYO//9977kqH3J8s/EA/HX6r5rFVsVkfv3xRGavPw1A2xYnAfUmuklvIvj8iI6oRjbUvmSteUnmhRCiPKuTeS8vLzw9G8/yJaKa2t4IgM2RPxl2fq7e8ojlddPWqbWQkwT2ntB8eLmXN5zdQF5RHk0cm9Deo73VzfQN88DVzobk7EK2na78A80rf4UTHpuJm72BOXd0rbCasxBCiEbELRj8OqlF5w4trv/28zNg5zfq92M+BFuXcpvc1CUAgN/2xmCxKFU67Mjgkeg1eg6nHCYiPcL6+KowzP5cWi5PLtoPwG09vTmWtRVQK+sDBJ8vuBjRSJP59IL0hg1ECCEaIauT+ZEjR7J9+3Yslsp7QEUjFtwfnPwhL5UxeWYA1p1ZR15R3hV2tML+H9Wv7adABYXtlkeqNxFGBY+qUUV9G52WUe18AVi6P7bCbX7dc46fd55Fo4GPb+koibwQQlwtOk1Tv+76Bur7s8eBhWDOAc9W6qowFRjexgdHo56Y9Dz+jahaITo3kxt9A/oCsPT0Uuvju0LPfEFRMQ//uJf0XDMdApzp0voseUV5NHVqWlovJ9hD7ZmPTGqcybwUwBNCiPKsTuZnzZpFYWEhjz32GIWFhbUZk6gPOr065B3osPtH/O19yS3KZePZjbXbTk4KHF+lft/h1nIvp+WnsS1mG1CzIfYlxnf0B+Dv8HjyzcVlXjsWn8mLSw4B8MTg5twQJiNLhBDiqtF+Chid1KHuEevrr11FUW8gAHSbAZXcdDbZ6BjbUZ3u9b9VxyiuYu98Sc/4sohlFFuKr7B1Ja7QM//G8qMcOJeBi50Ns6d2ZkXkMgDGhowtvYke7KnO829sFe1L5sxnFlx55SQhhLjeWF3tzM/Pjy1btjBu3DhatGjBwIEDCQwMRKstf39Ao9Hw0ksv1ShQUQc63AZbP0GTcpJROlfmEMfyiOWMCK6418Eqh38Fixl8O4BP23Ivr45aTZFSRCu3VoQ4h9S4ua5NXfFzNhGbkc/6Y4mMaOtDdkERiVkFPLhgL/lmC/2ae/LooGY1bksIIUQ9MjqoN4V3fgW7vlWLudaHyI3qUnQGxwoLuF7s8cFh/Lk/lgPnMvhp5xmm9Wx6xcP3D+iPs9GZxNxE1kSvse492O580dcKeuaX7o/h++3RAHw4pSNam3R2xu8EYEzomNLtQjwaaTIvc+aFEKJSVifziqLw8ccfc+zYMSwWC/PmzSu3jUajQVEUSeYbK50eBr8Ev9zB6BPbmOPjwpaYLaTnp+NicqmdNkqG2HecWuHLJUPsR4eMrpXmtFoNYzv48dWmCGb+coDHF+6n8KJieL7OJj6a0hGt1vrh/EIIIRpIt3vUZP7ESkg/Cy5N6r7NnXPUrx1uAePll+D1djLxf8Oa88pfR3hn1TFGtPHB09F42X0MOgNTW03l8/2f89XBrxgWNAytppoDJ0t65s05YM4DG3UKWWx6Hs//ro5Ie3RQMwa28OKLA18A0M2nG/4O/qWHKKnAn5JTSEaeGWfb8tPiGkLJ0nQyZ14IIcqzOpl/9913+fTTT9Hr9YwZM4awsDAcHK6xtW2vB63GgV8nQmP30ULfhONFWayOXs3kFpNrfuz4wxB3ALQ20Pamci/HZMewL3EfGjSMCKq90QCTugTw7ZZI8i4aZm9royPIw57/TWqHm72h1toSQghRjzybqzVfIjfC7u9gyKy6bS/jHBxXl06l2z1V2mVaz6b8uucc4bGZvLXiKB9M6XjFfaa2msoP4T9wKv0Ua6PXMixoWPXiNDqBVg+WInWovbOapL+18hi5hcV0aerKE0OaU1hcyC/HfwHgxrAbyxzCwajH09FIUlYBUck5dGjiUr0Y6khJz3xuUS7mYjM2FdTeEUKI65XVyfw333yDnZ0dmzdvplOnTrUZk6hPGg0MeRm+H8/YxBiOuzkxP3w+E5tNrPkb5oGf1a8tRoC9e7mXV0auBNTeAW/72lsarrm3I2tn9icz34ybvQF3eyO2Bt2VdxRCCNH4db9XTeb3fq/WftFfvue7RnbPVSvoB90AXi2rtItep+WNie2Y+PlWft8Xw81dm9ArtPx74MWcDE5MbT2VLw98yZcHv2RI0yHV653XaNTe+ewEdai9sz87I1P560AsGg28Mq4NOq2G5adXkZyXjJetF8Obll9dJtjDnqSsAiIbUTLvYOOABg0KChmFGXjYejR0SEII0WhYXQDv7Nmz3HDDDZLIXwtCBkDIAG7KzMBNY8OZrDP8cuKXmh2z2AwHF6nfVzDEXlGU0qXwamuI/cWCPOxpH+BCgKudJPJCCHEtaT4SnAIgNxnCl9RdO0UFsHe++n33e6u1a8cmLtzWPRCAl5YeprDoytX3b291Ow42DpxMO8k/Z/6pdrgXV7Qvtii8/Gc4ALd2D6StvzOKovB9+Pfqc61urfCGfcm8+ca0PJ1Oq8PRoE5vkCJ4QghRltXJvI+PD46Ol587Jq4ig/+LvaLwcFICAF8e+JKswizrj3fx2vLNhpR7eWvsVk6ln8KgNTA4sJ6KGAkhhLj66fTQ9U71+11z6q6dI3+q72OOvtCi+qutPDO8JR4OBk4lZjNn85XXkHc2OnNbq9sA9T3YolRz+b2LkvmFu85wJC4TJ5Oep4a1AGBX/C6Opx3HpDNxc/ObKzxE8PlkPqoRJfMg8+aFEKIyVifzEydOZPPmzeTn59dmPKKh+HeB1uO5MSubYEVPekE63x761vrjXWZteYti4ZO9nwBwS8tbSufDCSGEEFXSebpaj+XcLojdXzdtlNwo6HJXufexqnC2s+GFUa0A+GTdSU4nZV9xnzta34Gd3o7jacfZcHZD9Rq0cwMgLyOJ9/4+DsCTQ5uX1on54cgPAIxvNr7S991gqWgvhBBXFauT+Zdffhk3NzduvfVWkpOTazMm0VCGvILexp4nE+MAWHB0AfE58dU/TvLJy64tvyZ6DUdTj2JvY8897apWUEgIIYQo5eAFbSao39dF73zsPji7Qy0q12W61YeZ2MmfG8I8KCiy8NTiAxQVX763/dLeeUWp2lr1QGnP/L/hJ0nLNdPc24Hbzy+NF50ZzcZzGwG12F5lLk7mq9V2HXMyOgGQUSjJvBBCXMzqZP6JJ56gRYsWLF26lNDQUAYNGsSdd97J3XffXe4xY8aM2oxZ1BW3YBj+BgNy8+iSX0hBcQGf7vu0esdIOQ3zx6prywf2Lre2fJGliM/2fQbA9NbTcTW51lb0Qgghrifd71O/HlgIiUdr77iKAmv+q37f5kZw9LH6UBqNhv9Nao+jUc++M+l8XYXh9ne0vgNbvS1HU4+WJuBVcj6ZP3vuLACzxrbBRqd+zFtwZAEKCv0C+hHsHFzpIQLd7dBoILugiKTsgqq3XcdKhtlLz7wQQpRldTX7efPmodGoa3VnZWWxYcOGSrfVaDR8+20NhmyL+tPlTjTHV/BU9AZu9ffhr9N/Ma31NFq6VaGKb1qUmshnxYFnK5jyQ7lN/jz9J1GZUbgYXZjWelrtxy+EEOL60KS7Opf9+ApY9iTcuQK0VvdRXHB8JURuAp0RBr1Y48P5udgya1wbnlp8gA/XnGBQSy9a+jhVur2ryZVbW97Kd4e/451d79DFu0tpAbjLsdi6oQVcyGJ4G2/6NFOrvmcUZLD09FJAvVFwOUa9jgBXW86m5hGZlIOXo6nqJ1qHnA0yzF4IISpidTI/d+7c2oxDNBYaDYz7lLaf92Rkdg4rHez5YPcHfD3s68vvl34G5o2FzBjwaA7T/wT7ssvHFBQX8Pn+zwG4p909OBgc6uoshBBCXA9GvgMRG+HMdti/ADpfPlm9oqJCWH0+ge/1ELg2rXmMwKTO/qw6HM/aownMXHSAJQ/3waCv/MbDjHYzWBW5irNZZ3l528u81/+90g6Uymw4W8wgwFObzUtjWpc+/+uJX8kryqO5a3O6+3S/YqzBHg6cTc0jKiWHHiGXX1KvvsiceSGEqJjVyfz06dbPIRONnKMPjPmQx/6YwVp7O7bHbWdFxApGhVRSzTfjnNojn3EG3JvB9L/U+YyXWHRsEQm5CXjbeXNLy1vq+CSEEEJc81yawMAXYPV/1KHxLUaVu5FcLbu+gdTT6kosfWfWWpgajYa3bmzHng9TORKXyaf/nOT/zleZr4iTwYn3+r/HHavuYHX0ahYdX3TZ980TCVn8eDCbQXpo6WzG1dUOgOS8ZH469hMA01pPu+INAVCXp9t0IqlRLU9XmszLnHkhhCijFsajiWtSm4kEtLqRe9PVN87Xt79CbHZs2W0UBQ7/Bl/eoA6xdw1WE/kK5hfmmHP45tA3ADzY4UGMOmNdn4EQQojrQY8HwKcd5KVd6FW3Rm4qbHxb/X7Qi2CqfCi8NTwdjbwxsR0An284zZ7otMtu386zHTO7qDcU3tn1DuEp4RVuZy628H+/HCCxWB3t5oK6rGxURhTTVkwjMTcRH3sfRgVXbXm9IHf1RkBkUiNM5qVnXgghyrC6Z75EXFwcS5cu5fjx42RmZlZY/VTmzF+lRr3LvV9sY0t+AQdN8MKSm/h29I/oXIMhOwmWz4Sjf6rbereFWxeCk1+5wxRbinln1zukFaQR5BTE+Gbj6/lEhBBCXLN0ehjzMXwzGA78rK6iEtK/+sfZ8DbkZ6jvZ53qpqbLqHa+jOvgx58HYrlr7k5+mNGDDk1cKt3+9la3syt+F+vPruepDU/xy9hfys2fn73+FIdiMmh1vqCsJjeFA4n7eeSfR0kvSCfAIYAvh36JQWeoUozBnupNgca0PJ3MmRdCiIrVKJn/9NNPefrppzGbzaXPlSTzJUO5FEWRZP5qZeuKfsZq3l79HDdl72MPWcz9fiD3BI+Fo8sgL1VdtueG/4MbngJ9+Q8K+UX5PL/5edaeWQvAzC4z0WtrfA9JCCGEuCCgC3SboQ6TXz4THtwG+mqMAEs6oe4LMPwN0OrqJk7gjYltiU3PY3d0Grd/s4N5d3enS9OKV3bRaDS81uc1Jv81mXPZ55i1bRbv9nsX3fn4Dp3L4LN/TgHwyNge8BdssNHw9Op7yC8uoLV7a2YPno2HbdWnHoScX54uOiWXYouCTnvlofl1TXrmhRCiYlYPs1+3bh2PP/44JpOJ5557jl69egHw1Vdf8X//938EBQUB6hJ23333Xa0EKxqAcwBNbl7Ac23vBWC2swPhh39WE3nvtnDvP+p8xQoS+YyCDO5fcz9rz6zFRmvDu/3fZWDgwPo+AyGEENeDwf8FB29IOQULb1OHzVdFdhL8+SgoxdB8JIQMqNMwHU02zL+7O92D3cgqKOKOb3ewK6ryWJ2NzrzX/z30Wj1rotcwZdkUdsTtICY9j5m/7KfIojC6nS+dmzvwgbs7j3t7kF9cQF//vswdPrdaiTyo1fcNOi2FxRZi0/Nqerq1onRpOpkzL4QQZVidzH/88cdoNBr+/vtv3njjDcLCwgC49957effddzly5AjTp0/nu+++44YbbrCqjdmzZxMUFITJZKJHjx7s3LnzstsvXryYli1bYjKZaNeuHStWrLCqXVHehK6PM7TpUIo0Gp7zDyS33zNw73rw7VDh9rHZsdyx8g72Ju7F0caRr4Z+xYigEfUctRBCiOuGyRnGfw56Wzi1Fr4eAHEHLr/PidXwRS84+6+637DX6iVUe6OeeXd1o3eoOzmFxdzx7U62nU6udPt2nu14o88bONo4cjztOPesvodhP97JqfRI3FwTMPj+zKg/RjHXyR6LRsMEv358MugT7Gzsqh2bTquhacm8+UYy1L6kZz7HnIO52HyFrYUQ4vphdTK/c+dOOnfuTI8ePSp83Wg08sUXX2AymXj11VerffxFixYxc+ZMZs2axd69e+nQoQPDhw8nMTGxwu23bdvGrbfeyowZM9i3bx8TJkxgwoQJHD58uNpti/I0Gg2zes3Cy86LKMxMzdrNJwe/ZG/CXoosRQCk5qeyLGIZz29+npv+uomIjAi87byZP3I+3Xy6NfAZCCGEuOaFDYEZq8GlKaRHw7fDYP/P5bcrzIXl/wc/3Qw5SeDVGu5ZCx5h9RaqnUHPd3d244YwD/LMxdw2ZwcTZm/l602nOZuaW277YU1H8EDoHPTZfVEULVqHIziEfIDZ50PWnl1FkVJE12IdnyQk8WrQRGy0NlbHFnR+qH1jSeYdbBzQoA73l955IYS4QKNUVLGuCoxGIxMnTmThwoUA3HfffXz77bdkZ2dja2tbut3NN9/Mv//+y9mzZ6t1/B49etCtWzc+++wzACwWC02aNOHRRx/lueeeK7f9lClTyMnJYdmyZaXP9ezZk44dO/Lll1+W276goICCgoLSnzMzM2nSpAkZGRk4OdVuBdtryc64nTy87mHyi/NLn3O0ccTPwY8TaSdQuPDPqblrc2YPno2Pffnq9kIIIUSdyUuD3++Dk6vVn4NuAONF7+1JRyE1Qv2+x4Mw5GWwMdV7mAD55mKe/vUgyw7GcvEnsta+TtgbdaTmFJKaU0h6nrn0dT/PDPxD1nEscyd6jZ4RwSOY1noarZc9B5Ebwa8TOJYvSFtVJxKyiErJwc6gx95Yd/UDquNBzxhytAptCowYlYafxy+EuHrd0+0lbug8rqHDuKzMzEycnZ2vmJtaXYnMzc2NnJwLd2xdXdXiLWfOnKFFiwtrpxYXF5OSklKtYxcWFrJnzx6ef/750ue0Wi1Dhgxh+/btFe6zfft2Zs4suybs8OHDWbJkSYXbv/XWW7zyyivViktAd9/urJy0km2x29hybgvb4raRUZDB8bTjALRwbUFf/7708e9DJ69OUuxOCCFE/bN1hVsXwcb/qcvNRW0uv42DN0z4HJoNqf/4LmKy0fHprZ14aXQr/g6PZ8WheHZEpnAkLrPcto4mPQ8PbMadvYMw2dxGdGY09jb2F+bFu4WoyXzsPmCf1TE1B5rrgGKg/CCBBhFk9ibcaCTcWHDljYUQ4jKSM2KvvNFVwupMKzAwsExve9u2bVEUhWXLlpUm89nZ2WzevJmAgIBqHTs5OZni4mK8vb3LPO/t7c2xY8cq3Cc+Pr7C7ePj4yvc/vnnny+T/Jf0zIsr87D1YFzoOMaFjqPYUszhlMPE5cTR2aszXnZeDR2eEEIIAVotDHwemg8vP3deb4TmI8DOrWFiq4CXk4lpvYKY1iuI5OwCtp5KRqfV4GZvwN3eiJu9AVc7G/S6CzMkmzo1LXuQIbOgSXcoqlnCa1HgSGwGOYXFNTpObbrDkk24JbbMCEAhhLBGm5CeDR1CrbE6me/fvz8ffvghCQkJeHt7M3r0aOzt7XnhhReIj48nMDCQ+fPnk5qayi233FKbMdcKo9GI0ViNZWtEhXRaHR08O9DBs+JCeEIIIUSD8u+sPq4iHg5Gxnf0r/6Otq7Q8bYat68F2tb4KLVvVEMHIIQQjYzVyfzNN9/Mvn372L9/P8OHD8fNzY0PPviABx54gA8++ABQ15gPCgqq9nB2Dw8PdDodCQkJZZ5PSEjAx6fi+dc+Pj7V2l4IIYQQQgghhLhaWV3Nvlu3bqxZs4bhw4eXPnfvvfeya9cunnnmGe655x4++OAD9u3bh5tb9YaxGQwGunTpwrp160qfs1gsrFu3rnQ9+0v16tWrzPYAa9asqXR7IYQQQgghhBDialXr1ck6d+5M5841H842c+ZMpk+fTteuXenevTsfffQROTk53HXXXQDccccd+Pv789ZbbwHw+OOP079/f95//31Gjx7NwoUL2b17N19//XWV2isp6p+ZWb7gjBBCCCGEEEIIUR9KctIrLTzXaEuNT5kyhaSkJP773/8SHx9Px44dWbVqVWmRuzNnzqDVXhhY0Lt3b3766SdefPFFXnjhBcLCwliyZAlt21Zt1ldWVhaAFMETQgghhBBCCNHgsrKycHZ2rvR1q9eZv9ZYLBZiY2NxdHREo2nc65eWVN4/e/bsZdcdFI2bXMdrg1zHa4Ncx2uDXMdrh1zLa4Ncx2uDXMf6pygKWVlZ+Pn5lenAvlSj7Zmvb1qtttpL6DU0Jycn+YO6Bsh1vDbIdbw2yHW8Nsh1vHbItbw2yHW8Nsh1rF+X65EvYXUBPCGEEEIIIYQQQjQMSeaFEEIIIYQQQoirjCTzVyGj0cisWbMwGo0NHYqoAbmO1wa5jtcGuY7XBrmO1w65ltcGuY7XBrmOjZcUwBNCCCGEEEIIIa4y0jMvhBBCCCGEEEJcZSSZF0IIIYQQQgghrjKSzAshhBBCCCGEEFcZSeaFEEIIIYQQQoirjCTzjdTs2bMJCgrCZDLRo0cPdu7cedntFy9eTMuWLTGZTLRr144VK1bUU6TicqpzHefMmcMNN9yAq6srrq6uDBky5IrXXdSP6v49lli4cCEajYYJEybUbYCiSqp7HdPT03n44Yfx9fXFaDTSvHlz+b+1Eajudfzoo49o0aIFtra2NGnShCeffJL8/Px6ilZUZNOmTYwdOxY/Pz80Gg1Lliy54j4bNmygc+fOGI1GmjVrxrx58+o8TnF51b2Ov//+O0OHDsXT0xMnJyd69erF33//XT/Bisuy5m+yxNatW9Hr9XTs2LHO4hOVk2S+EVq0aBEzZ85k1qxZ7N27lw4dOjB8+HASExMr3H7btm3ceuutzJgxg3379jFhwgQmTJjA4cOH6zlycbHqXscNGzZw6623sn79erZv306TJk0YNmwYMTEx9Ry5uFh1r2OJqKgonnrqKW644YZ6ilRcTnWvY2FhIUOHDiUqKopff/2V48ePM2fOHPz9/es5cnGx6l7Hn376ieeee45Zs2Zx9OhRvv32WxYtWsQLL7xQz5GLi+Xk5NChQwdmz55dpe0jIyMZPXo0AwcOZP/+/TzxxBPcc889kgg2sOpex02bNjF06FBWrFjBnj17GDhwIGPHjmXfvn11HKm4kupeyxLp6enccccdDB48uI4iE1ekiEane/fuysMPP1z6c3FxseLn56e89dZbFW4/efJkZfTo0WWe69Gjh3L//ffXaZzi8qp7HS9VVFSkODo6KvPnz6+rEEUVWHMdi4qKlN69eyvffPONMn36dGX8+PH1EKm4nOpexy+++EIJCQlRCgsL6ytEUQXVvY4PP/ywMmjQoDLPzZw5U+nTp0+dximqDlD++OOPy27zzDPPKG3atCnz3JQpU5Thw4fXYWSiOqpyHSvSunVr5ZVXXqn9gITVqnMtp0yZorz44ovKrFmzlA4dOtRpXKJi0jPfyBQWFrJnzx6GDBlS+pxWq2XIkCFs3769wn22b99eZnuA4cOHV7q9qHvWXMdL5ebmYjabcXNzq6swxRVYex1fffVVvLy8mDFjRn2EKa7Amuv4559/0qtXLx5++GG8vb1p27Ytb775JsXFxfUVtriENdexd+/e7Nmzp3QofkREBCtWrGDUqFH1ErOoHfI559pksVjIysqSzzlXqblz5xIREcGsWbMaOpTrmr6hAxBlJScnU1xcjLe3d5nnvb29OXbsWIX7xMfHV7h9fHx8ncUpLs+a63ipZ599Fj8/v3IfYET9seY6btmyhW+//Zb9+/fXQ4SiKqy5jhEREfzzzz9MnTqVFStWcOrUKR566CHMZrN8cGkg1lzH2267jeTkZPr27YuiKBQVFfHAAw/IMPurTGWfczIzM8nLy8PW1raBIhM18d5775Gdnc3kyZMbOhRRTSdPnuS5555j8+bN6PWSTjYk6ZkXohF6++23WbhwIX/88Qcmk6mhwxFVlJWVxbRp05gzZw4eHh4NHY6oAYvFgpeXF19//TVdunRhypQp/Oc//+HLL79s6NBENWzYsIE333yTzz//nL179/L777+zfPlyXnvttYYOTYjr2k8//cQrr7zCL7/8gpeXV0OHI6qhuLiY2267jVdeeYXmzZs3dDjXPbmV0sh4eHig0+lISEgo83xCQgI+Pj4V7uPj41Ot7UXds+Y6lnjvvfd4++23Wbt2Le3bt6/LMMUVVPc6nj59mqioKMaOHVv6nMViAUCv13P8+HFCQ0PrNmhRjjV/j76+vtjY2KDT6Uqfa9WqFfHx8RQWFmIwGOo0ZlGeNdfxpZdeYtq0adxzzz0AtGvXjpycHO677z7+85//oNVKn8bVoLLPOU5OTtIrfxVauHAh99xzD4sXL5bRh1ehrKwsdu/ezb59+3jkkUcA9bOOoijo9XpWr17NoEGDGjjK64e8izUyBoOBLl26sG7dutLnLBYL69ato1evXhXu06tXrzLbA6xZs6bS7UXds+Y6Arzzzju89tprrFq1iq5du9ZHqOIyqnsdW7ZsyaFDh9i/f3/pY9y4caUVmJs0aVKf4YvzrPl77NOnD6dOnSq9GQNw4sQJfH19JZFvINZcx9zc3HIJe8kNGkVR6i5YUavkc8614+eff+auu+7i559/ZvTo0Q0djrCCk5NTuc86DzzwAC1atGD//v306NGjoUO8vjRwAT5RgYULFypGo1GZN2+ecuTIEeW+++5TXFxclPj4eEVRFGXatGnKc889V7r91q1bFb1er7z33nvK0aNHlVmzZik2NjbKoUOHGuoUhFL96/j2228rBoNB+fXXX5W4uLjSR1ZWVkOdglCqfx0vJdXsG4fqXsczZ84ojo6OyiOPPKIcP35cWbZsmeLl5aW8/vrrDXUKQqn+dZw1a5bi6Oio/Pzzz0pERISyevVqJTQ0VJk8eXJDnYJQFCUrK0vZt2+fsm/fPgVQPvjgA2Xfvn1KdHS0oiiK8txzzynTpk0r3T4iIkKxs7NTnn76aeXo0aPK7NmzFZ1Op6xataqhTkEo1b+OP/74o6LX65XZs2eX+ZyTnp7eUKcgzqvutbyUVLNvOJLMN1KffvqpEhgYqBgMBqV79+7Kv//+W/pa//79lenTp5fZ/pdfflGaN2+uGAwGpU2bNsry5cvrOWJRkepcx6ZNmypAucesWbPqP3BRRnX/Hi8myXzjUd3ruG3bNqVHjx6K0WhUQkJClDfeeEMpKiqq56jFpapzHc1ms/Lyyy8roaGhislkUpo0aaI89NBDSlpaWv0HLkqtX7++wve7kms3ffp0pX///uX26dixo2IwGJSQkBBl7ty59R63KKu617F///6X3V40HGv+Ji8myXzD0SiKjDMTQgghhBBCCCGuJjJnXgghhBBCCCGEuMpIMi+EEEIIIYQQQlxlJJkXQgghhBBCCCGuMpLMCyGEEEIIIYQQVxlJ5oUQQgghhBBCiKuMJPNCCCGEEEIIIcRVRpJ5IYQQQgghhBDiKiPJvBBCCCGEEEIIcZWRZF4IIYS4SFRUFBqNhqCgoIYOpcEEBQWh0WhKH0OGDGnokK5KAwYMQKPRsGHDhhodx2Qylbked955Z63EJ4QQ4uomybwQQojrSkmiGhUV1dCh1JmXX34ZjUbDyy+/XKPjTJo0ienTpzN8+PDaCUxYZdq0aUyfPp0+ffo0dChCCCEaEX1DByCEEEI0Jv7+/hw9ehQbG5uGDqXBvffee9f1CIXGYs6cOQDMmzePrVu3NnA0QgghGgtJ5oUQQoiL2NjY0LJly4YOQwghhBDismSYvRBCiOvCvHnz0Gg0REdHAxAcHFxmHnLJvObLzZkv2RZgwYIFdO/eHQcHBzw9Pbn11ls5c+YMAIqi8Nlnn9GxY0fs7e3x8PDgzjvvJDExsdL4Tpw4wf33309oaCgmkwlnZ2f69evHggULqnWeGo2GV155BYBXXnmlzuZa79mzhylTphAQEIDBYMDJyYmQkBAmTZrE0qVLK91n6tSpBAYGYjQacXNzY/jw4axYsaLSdoqKivjuu+8YMmQIHh4eGI1GAgICGDJkCJ9++mmF23/55Zf07t0bZ2dnTCYTYWFhPPbYY8TExFTYxsXX9bfffqNv3744OTlhb29Pnz59Lhvf2bNnufvuu/H19S1t6z//+Q95eXmV7pORkcGLL75Iu3btsLe3x2g04ufnR58+ffjvf/+L2WyudF8hhBCihPTMCyGEuC40a9aM6dOn8+uvv5KTk8OkSZNwcHAofd3Hx6fKx3r++ed577336NevHyNHjmTnzp0sXLiQrVu3cuDAAR544AH+/PNPBgwYQEhICFu3bmX+/Pns27ePXbt2YTAYyhxv8eLF3HHHHeTn59OyZUtGjRpFRkYGO3bsYNq0afzzzz989913VYpt+vTp7N+/nwMHDtChQwc6duxY+lrfvn2rfI6Xs27dOkaOHInZbKZDhw706tWL4uJiYmJiWL58OcXFxYwfP77MPh9//DEzZ87EYrHQsWNHevToQXx8PBs2bGD16tW88sor/Pe//y2zT0ZGBmPGjGHLli3Y2NjQu3dv/Pz8iI+P5+DBg6xbt45HH320dPuCggLGjBnD2rVrMZlMDBw4ECcnJ7Zt28ann37Kzz//zN9//03nzp0rPK9Zs2bx2muv0bt3b0aNGsWxY8fYtm0bY8aM4bfffmPixIlltj927Bj9+/cnMTERX19fxo0bR05ODh9++CHr16+vsI3c3Fz69u3L4cOH8fT0ZPDgwdjb2xMfH1/a3syZM3FxcbHiygghhLiuKEIIIcR1pGnTpgqgREZGVvh6ZGSkAihNmzYt9xqgAIq7u7uyf//+0udzc3OVvn37KoDSrl07JTQ0VImKiip9PSkpSWnWrJkCKAsWLChzzIMHDypGo1ExmUzKb7/9Vua1qKgopV27dgqgzJ8/v8rnOGvWLAVQZs2aVeV9Lnal39HAgQMrPBdFUZT09HRl+/btZZ5btWqVotFoFA8PD2Xjxo1lXjt48KASEBCgAMqGDRvKvHbjjTcqgNKpU6dysZjNZmXJkiVlnnv22WcVQAkNDS2zfWFhoTJjxgwFUIKDg5WCgoIy+5VcVxcXF+Xff/8t81rJ77J58+blzrVbt24KoEyePFnJy8srfT46OloJDQ0tPe769etLX5s/f74CKCNHjlQKCwvLHK+4uFjZsGFDufhKzJ07VwGU6dOnV/i6EEKI64sMsxdCCCGq6dVXX6VDhw6lP9va2jJz5kwADh06xCeffELTpk1LX/fw8ODBBx8E1F7ti73xxhsUFBTw+uuvc+ONN5Z5rWnTpnz77bcAfPLJJ3VyLtZISEgAYNSoUeVec3Z2pmfPnmWemzVrFoqi8OWXX9KvX78yr7Vr144PPvgAoMyw+QMHDvD7779jMpn466+/yk170Ov1ZXr/8/PzmT17NgAffvhhme1tbGz45JNP8Pb2JjIykl9//bXC83r11Vfp0aNHmeeef/55nJ2dOXHiBGfPni19fuvWrezatQt7e3s+//xzTCZT6WuBgYG89957FbZR8rsbOnRouSKLWq2W/v37lxu5IYQQQlREknkhhBCimipKYsPCwgA1yRw2bFilr8fGxpY+Z7FYWLlyJQBTpkypsK2uXbvi4ODAvn37yM/Pr3HstaF79+4ATJ06lS1btlBUVFTptsnJyezcuRNbW1vGjh1b4TYDBgwAYNu2baXPrVq1CoDRo0fj7+9/xZh2795NdnY2bm5uFbZjZ2fHLbfcAlDpEPiK9jMajYSEhACUmXNfUmNhxIgRuLu7l9tv/PjxODs7l3u+W7duALzzzjt8//33pKamXuHMhBBCiIpJMi+EEEJUU2BgYLnnSubf+/r6oteXL0nj6OgIUCYhT0lJITMzE4AmTZqUKVZX8tBqtWRnZ2OxWEhJSamL06m2t956i86dO7Ny5UpuuOEGnJyc6Nu3Ly+++CJHjx4ts21kZCSKopCXl4fRaKzwHL28vABISkoq3a+kUGFVVxYoSbSDg4Mr3SY0NLTMtpeq6LoCODk5AWWv3blz5y7bXmVFFAcMGMCzzz5LYmIi06dPx8PDgxYtWnD33XezdOlSLBZLpfELIYQQF5MCeEIIIUQ1abWV3wu/3GuXujhxmz59+hW3NxqNVT52XfLx8WH37t1s3LiRtWvXsnXrVnbs2MHWrVt58803eeutt3j22WeBC+fo4ODApEmTGjLsK6rOtauJt99+mwceeIC//vqLLVu2sHXrVubOncvcuXPp1q0b69evx97evl5iEUIIcfWSZF4IIYRoIB4eHtja2pKXl8d7772Hh4dHQ4dUZRqNhgEDBpQOkc/Pz2fevHk8/PDDvPDCC9x0002Ehv5/e/cX0vQax3H8fRJKqRDJf5iGcRTTYOjAmzLKwoqiFCq6GbZdJKWtAl3eRFJ4Fd1YdBWYpYQUlRhoGEtmf2bMwLRCKNIsQhQkWCUyc13ERjvbDp6Tp53l53X52/N79v1tV9/f8zzf759kZGT4xzc2Ns45Yfatkg8NDc1pvG8r/vDwcNgxb968CRj7M3xzjIyMhB3j210QSmZmJlar1V+N3+VyYTKZcLlcnD171t9eUEREJBxtsxcRkQXFV1zs7855/yoxMTGUlJQAcP369XmbNxLPGBsby6FDhzAYDMzOzjIwMABAWloaBoMBt9vtPwc/F9u3bwego6MjoM5AOL7aApOTk7S3twd9PjU1RWtrKwDFxcVzjiOcjRs3At/P9oc6997e3s7Hjx/nPF9hYSGVlZUA9Pf3/3R8IiLy+1MyLyIiC0p6ejoAL168iHAk39XV1bF48WJsNhtXrlwJeWb6+fPn3Lp1a85z/tfPeO7cOUZHR4OuDw0N8erVK4CAav719fUAWCwW7ty5E3Sf1+vlyZMndHV1+a/l5+dTWlrK1NQUpaWlQd83MzMTkLTHxsZSVVUFQHV1dcCquMfj4dixY4yNjbF69Wr27t37bx47wIYNGzAajXz69Imqqiqmp6f9n717946ampqQ992+fZuenp6g/9nj8fhfdvz424mIiISjbfYiIrKg7Nmzh+7ubkwmE1u3biUhIQEAm81GTk7OL4/HaDTS0tKC2WzGbDZz8uRJ8vLySEpKYnJyksHBQd6/f8/+/fuDWteFs23bNpYuXUpbWxtFRUVkZ2cTExPD+vXrsVgsPx1zfX09NpuNNWvWkJubS1xcHB8+fPBXti8vL8doNPrH79q1i4aGBqqrq9m9ezdZWVnk5OQQHx/PxMQEz549Y3x8nNra2oBOAJcvX2bHjh309vaSnZ3NunXrSEtLY2xsjMHBQSYmJvB6vf7xp0+fpq+vD7vdTm5uLsXFxSxfvhyn08no6CgrVqzgxo0b89b6rbm5mU2bNtHa2kpPTw9FRUV8+fKF+/fvYzAYSExMxOl0BtzjcDhoaGggMTGRgoICkpOTcbvd9Pb2Mj4+zsqVKzlx4sS8xCciIr83JfMiIrKgHD58GLfbTUtLCx0dHf4K5SaTKSLJPMC+ffsoLCzk/Pnz3Lt3j0ePHvH161dSUlLIysriyJEj/2g1OSUlhc7OTs6cOcPTp09xOp3Mzs4yMzMzL8n8xYsXsdvtuFwuHA4Hnz9/JjU1lZKSEioqKgL6v/scPXqUzZs3c+HCBbq7u7Hb7SxatIjU1FQKCgrYuXNnUIG8hIQEHA4HjY2NXLt2jf7+fh4/fkxycjL5+fmUlZUFjF+yZAl3797l0qVLXL16lQcPHjA9PU1GRgZWq5Xa2tp5OS/vk5eXR19fH3V1dXR2dtLW1kZ6ejpWq5VTp06FbGFoNpuJi4vj4cOHvHz5EofDQXx8PKtWreL48eNUVFSEbHUnIiLyV394f3ylLSIiIgteZmYmb9++ZXh4OGR7NYmMpqYmLBYLBw4coKmpKdLhiIhIhGllXkREREKqqalh2bJlrF27FpvNFulwFqyDBw/i8Xh4/fp1pEMREZH/ESXzIiIiEtLNmzcB2LJli5L5CGpubg4osCciIgLaZi8iIiIiIiISddSaTkRERERERCTKKJkXERERERERiTJK5kVERERERESijJJ5ERERERERkSijZF5EREREREQkyiiZFxEREREREYkySuZFREREREREooySeREREREREZEoo2ReREREREREJMp8A0DlifXEuGt+AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x1000 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(5,1,constrained_layout=True, figsize=(10,10))\n",
    "for c in range(0, 5):   \n",
    "    for n in range(0, 3):\n",
    "        idx=np.random.randint(0,10)\n",
    "        ax[c].plot(t, X[Y==c][idx])        \n",
    "        ax[c].set_xlabel('time t [seconds]', fontsize=16)\n",
    "        ax[c].set_ylabel('amplitude', fontsize=16)\n",
    "    ax[c].set_title('class '+str(c), fontsize=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "qD6ckUN0OZjO"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=0)\n",
    "X_train, X_val, Y_train, Y_val = train_test_split(X_train, Y_train, test_size=0.1, random_state=0)\n",
    "#note: you may need to add a channel axis to the data if the network is CNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((2764,), (308,))"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_train.shape, Y_val.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Activation, MaxPooling1D, LayerNormalization,Flatten\n",
    "from keras.activations import softmax\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import Input, Conv1D\n",
    "from tensorflow.keras.layers import Add\n",
    "from keras.layers import Reshape\n",
    "from keras.layers import GlobalAveragePooling1D\n",
    "from keras.models import Model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                Output Shape                 Param #   Connected to                  \n",
      "==================================================================================================\n",
      " input_1 (InputLayer)        [(None, 187, 1)]             0         []                            \n",
      "                                                                                                  \n",
      " conv1d (Conv1D)             (None, 187, 32)              192       ['input_1[0][0]']             \n",
      "                                                                                                  \n",
      " activation (Activation)     (None, 187, 32)              0         ['conv1d[0][0]']              \n",
      "                                                                                                  \n",
      " conv1d_1 (Conv1D)           (None, 187, 32)              5152      ['activation[0][0]']          \n",
      "                                                                                                  \n",
      " activation_1 (Activation)   (None, 187, 32)              0         ['conv1d_1[0][0]']            \n",
      "                                                                                                  \n",
      " global_average_pooling1d (  (None, 32)                   0         ['activation_1[0][0]']        \n",
      " GlobalAveragePooling1D)                                                                          \n",
      "                                                                                                  \n",
      " max_pooling1d (MaxPooling1  (None, 93, 32)               0         ['activation[0][0]']          \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " add (Add)                   (None, 93, 32)               0         ['global_average_pooling1d[0][\n",
      "                                                                    0]',                          \n",
      "                                                                     'max_pooling1d[0][0]']       \n",
      "                                                                                                  \n",
      " layer_normalization (Layer  (None, 93, 32)               64        ['add[0][0]']                 \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " conv1d_2 (Conv1D)           (None, 93, 32)               5152      ['layer_normalization[0][0]'] \n",
      "                                                                                                  \n",
      " activation_2 (Activation)   (None, 93, 32)               0         ['conv1d_2[0][0]']            \n",
      "                                                                                                  \n",
      " global_average_pooling1d_1  (None, 32)                   0         ['activation_2[0][0]']        \n",
      "  (GlobalAveragePooling1D)                                                                        \n",
      "                                                                                                  \n",
      " max_pooling1d_1 (MaxPoolin  (None, 46, 32)               0         ['add[0][0]']                 \n",
      " g1D)                                                                                             \n",
      "                                                                                                  \n",
      " add_1 (Add)                 (None, 46, 32)               0         ['global_average_pooling1d_1[0\n",
      "                                                                    ][0]',                        \n",
      "                                                                     'max_pooling1d_1[0][0]']     \n",
      "                                                                                                  \n",
      " layer_normalization_1 (Lay  (None, 46, 32)               64        ['add_1[0][0]']               \n",
      " erNormalization)                                                                                 \n",
      "                                                                                                  \n",
      " conv1d_3 (Conv1D)           (None, 46, 32)               5152      ['layer_normalization_1[0][0]'\n",
      "                                                                    ]                             \n",
      "                                                                                                  \n",
      " activation_3 (Activation)   (None, 46, 32)               0         ['conv1d_3[0][0]']            \n",
      "                                                                                                  \n",
      " global_average_pooling1d_2  (None, 32)                   0         ['activation_3[0][0]']        \n",
      "  (GlobalAveragePooling1D)                                                                        \n",
      "                                                                                                  \n",
      " max_pooling1d_2 (MaxPoolin  (None, 23, 32)               0         ['add_1[0][0]']               \n",
      " g1D)                                                                                             \n",
      "                                                                                                  \n",
      " add_2 (Add)                 (None, 23, 32)               0         ['global_average_pooling1d_2[0\n",
      "                                                                    ][0]',                        \n",
      "                                                                     'max_pooling1d_2[0][0]']     \n",
      "                                                                                                  \n",
      " layer_normalization_2 (Lay  (None, 23, 32)               64        ['add_2[0][0]']               \n",
      " erNormalization)                                                                                 \n",
      "                                                                                                  \n",
      " conv1d_4 (Conv1D)           (None, 23, 32)               5152      ['layer_normalization_2[0][0]'\n",
      "                                                                    ]                             \n",
      "                                                                                                  \n",
      " activation_4 (Activation)   (None, 23, 32)               0         ['conv1d_4[0][0]']            \n",
      "                                                                                                  \n",
      " flatten (Flatten)           (None, 736)                  0         ['activation_4[0][0]']        \n",
      "                                                                                                  \n",
      " dense (Dense)               (None, 5)                    3685      ['flatten[0][0]']             \n",
      "                                                                                                  \n",
      " activation_5 (Activation)   (None, 5)                    0         ['dense[0][0]']               \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 24677 (96.39 KB)\n",
      "Trainable params: 24677 (96.39 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "inp=Input(shape=(187,1))\n",
    "#---1st Layer-------\n",
    "D1=Conv1D(filters=32, kernel_size=5,strides=1,padding='same')(inp)\n",
    "A1=Activation(\"relu\")(D1)\n",
    "#---Max_Pooling------\n",
    "M1=MaxPooling1D(pool_size=2,strides=2)(A1)\n",
    "#---2nd Layer---------\n",
    "D2=Conv1D(filters=32, kernel_size=5,strides=1,padding='same')(A1)\n",
    "A2=Activation(\"relu\")(D2)\n",
    "#GAP_A2=Flatten()(A2)\n",
    "GAP_A2 = GlobalAveragePooling1D()(A2)\n",
    "S21=Add()([GAP_A2,M1])\n",
    "#---Max_Pooling-----\n",
    "M2=MaxPooling1D(pool_size=2,strides=2)(S21)\n",
    "#---3rd Layer-------\n",
    "LN3=LayerNormalization()(S21)\n",
    "D3=Conv1D(filters=32, kernel_size=5,strides=1,padding='same')(LN3)\n",
    "A3=Activation(\"relu\")(D3)\n",
    "GAP_A3 = GlobalAveragePooling1D()(A3)\n",
    "S32=Add()([GAP_A3,M2])\n",
    "#---Max Pooling-----\n",
    "M3=MaxPooling1D(pool_size=2,strides=2)(S32)\n",
    "#---4th Layer-------\n",
    "LN4=LayerNormalization()(S32)\n",
    "D4=Conv1D(filters=32, kernel_size=5,strides=1,padding='same')(LN4)\n",
    "A4=Activation(\"relu\")(D4)\n",
    "GAP_A4 = GlobalAveragePooling1D()(A4)\n",
    "S43=Add()([GAP_A4,M3])\n",
    "#---5th Layer-------\n",
    "LN5=LayerNormalization()(S43)\n",
    "D5=Conv1D(filters=32, kernel_size=5,strides=1,padding='same')(LN5)\n",
    "A5=Activation(\"relu\")(D5)\n",
    "F1=Flatten()(A5)\n",
    "#---6th Layer-------\n",
    "D6=Dense(5)(F1)\n",
    "A6=Activation(softmax)(D6)\n",
    "#----\n",
    "model=Model(inputs=inp,outputs=A6)\n",
    "model.compile(loss='sparse_categorical_crossentropy', optimizer=Adamax(learning_rate=0.001), metrics=['accuracy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(TensorShape([None, 32]),\n",
       " TensorShape([None, 93, 32]),\n",
       " TensorShape([None, 187, 32]))"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GAP_A2.shape, M1.shape, A2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorShape([None, 23, 32])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S43.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(TensorShape([None, 5]), TensorShape([None, 5]))"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D6.shape,A6.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zyH0UvuMOZjR"
   },
   "source": [
    "### Define the MLP model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "OOXjQJ_OOZjS"
   },
   "outputs": [],
   "source": [
    "loss_train_list=[]\n",
    "loss_val_list=[]\n",
    "acc_train_list=[]\n",
    "acc_val_list=[]\n",
    "epoch_save=-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2764,)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2764, 187)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train=np.array(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2764, 187, 1)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train=X_train.reshape(-1,187,1)\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_val=np.array(X_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[0.        ],\n",
       "        [0.01980198],\n",
       "        [0.08712871],\n",
       "        ...,\n",
       "        [0.        ],\n",
       "        [0.        ],\n",
       "        [0.        ]],\n",
       "\n",
       "       [[0.70711297],\n",
       "        [0.38075313],\n",
       "        [0.18828452],\n",
       "        ...,\n",
       "        [0.        ],\n",
       "        [0.        ],\n",
       "        [0.        ]],\n",
       "\n",
       "       [[0.79079497],\n",
       "        [0.8242678 ],\n",
       "        [0.79288703],\n",
       "        ...,\n",
       "        [0.        ],\n",
       "        [0.        ],\n",
       "        [0.        ]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[0.98563218],\n",
       "        [0.73563218],\n",
       "        [0.27873564],\n",
       "        ...,\n",
       "        [0.        ],\n",
       "        [0.        ],\n",
       "        [0.        ]],\n",
       "\n",
       "       [[1.        ],\n",
       "        [0.92838877],\n",
       "        [0.71099746],\n",
       "        ...,\n",
       "        [0.        ],\n",
       "        [0.        ],\n",
       "        [0.        ]],\n",
       "\n",
       "       [[1.        ],\n",
       "        [0.9173913 ],\n",
       "        [0.75652176],\n",
       "        ...,\n",
       "        [0.        ],\n",
       "        [0.        ],\n",
       "        [0.        ]]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_val.reshape(-1,187,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((187, 1), (187,))"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train=X_train.reshape(-1,187,1)\n",
    "X_train[0].shape, X_val[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train=X_train.reshape(2764, 187,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 0\n",
      "44/44 [==============================] - 6s 90ms/step - loss: 1.4631 - accuracy: 0.3372 - val_loss: 1.4020 - val_accuracy: 0.4123\n",
      "epoch 1\n",
      "44/44 [==============================] - 3s 75ms/step - loss: 1.2683 - accuracy: 0.4721 - val_loss: 1.2724 - val_accuracy: 0.5487\n",
      "epoch 2\n",
      "44/44 [==============================] - 3s 70ms/step - loss: 1.1421 - accuracy: 0.5485 - val_loss: 1.1156 - val_accuracy: 0.6039\n",
      "epoch 3\n",
      "44/44 [==============================] - 3s 74ms/step - loss: 1.0158 - accuracy: 0.6281 - val_loss: 1.0203 - val_accuracy: 0.6071\n",
      "epoch 4\n",
      "44/44 [==============================] - 3s 74ms/step - loss: 0.9061 - accuracy: 0.6596 - val_loss: 0.9504 - val_accuracy: 0.6623\n",
      "epoch 5\n",
      "44/44 [==============================] - 3s 67ms/step - loss: 0.9058 - accuracy: 0.6563 - val_loss: 0.9013 - val_accuracy: 0.6623\n",
      "epoch 6\n",
      "44/44 [==============================] - 4s 87ms/step - loss: 0.8331 - accuracy: 0.6910 - val_loss: 0.7932 - val_accuracy: 0.6883\n",
      "epoch 7\n",
      "44/44 [==============================] - 3s 78ms/step - loss: 0.7851 - accuracy: 0.7033 - val_loss: 0.8059 - val_accuracy: 0.6818\n",
      "epoch 8\n",
      "44/44 [==============================] - 3s 69ms/step - loss: 0.7650 - accuracy: 0.7109 - val_loss: 0.7714 - val_accuracy: 0.7013\n",
      "epoch 9\n",
      "44/44 [==============================] - 3s 70ms/step - loss: 0.7302 - accuracy: 0.7207 - val_loss: 0.7474 - val_accuracy: 0.6851\n",
      "epoch 10\n",
      "44/44 [==============================] - 4s 86ms/step - loss: 0.7005 - accuracy: 0.7363 - val_loss: 0.7184 - val_accuracy: 0.7143\n",
      "epoch 11\n",
      "44/44 [==============================] - 3s 70ms/step - loss: 0.6922 - accuracy: 0.7363 - val_loss: 0.7422 - val_accuracy: 0.7208\n",
      "epoch 12\n",
      "44/44 [==============================] - 3s 65ms/step - loss: 0.6997 - accuracy: 0.7424 - val_loss: 0.7048 - val_accuracy: 0.7338\n",
      "epoch 13\n",
      "44/44 [==============================] - 4s 82ms/step - loss: 0.6613 - accuracy: 0.7489 - val_loss: 0.6859 - val_accuracy: 0.7435\n",
      "epoch 14\n",
      "44/44 [==============================] - 3s 68ms/step - loss: 0.6173 - accuracy: 0.7732 - val_loss: 0.6781 - val_accuracy: 0.7240\n",
      "epoch 15\n",
      "44/44 [==============================] - 3s 66ms/step - loss: 0.6352 - accuracy: 0.7590 - val_loss: 0.6089 - val_accuracy: 0.7695\n",
      "epoch 16\n",
      "44/44 [==============================] - 3s 66ms/step - loss: 0.5581 - accuracy: 0.7981 - val_loss: 0.5927 - val_accuracy: 0.7890\n",
      "epoch 17\n",
      "44/44 [==============================] - 3s 67ms/step - loss: 0.5487 - accuracy: 0.7894 - val_loss: 0.6115 - val_accuracy: 0.7695\n",
      "epoch 18\n",
      "44/44 [==============================] - 3s 66ms/step - loss: 0.5880 - accuracy: 0.7779 - val_loss: 0.6579 - val_accuracy: 0.7630\n",
      "epoch 19\n",
      "44/44 [==============================] - 3s 77ms/step - loss: 0.5563 - accuracy: 0.7923 - val_loss: 0.5727 - val_accuracy: 0.7987\n",
      "epoch 20\n",
      "44/44 [==============================] - 3s 74ms/step - loss: 0.5587 - accuracy: 0.7931 - val_loss: 0.5979 - val_accuracy: 0.7857\n",
      "epoch 21\n",
      "44/44 [==============================] - 3s 65ms/step - loss: 0.5088 - accuracy: 0.8111 - val_loss: 0.5329 - val_accuracy: 0.8052\n",
      "epoch 22\n",
      "44/44 [==============================] - 3s 66ms/step - loss: 0.5116 - accuracy: 0.8064 - val_loss: 0.5553 - val_accuracy: 0.7792\n",
      "epoch 23\n",
      "44/44 [==============================] - 3s 66ms/step - loss: 0.5007 - accuracy: 0.8086 - val_loss: 0.7293 - val_accuracy: 0.7143\n",
      "epoch 24\n",
      "44/44 [==============================] - 3s 67ms/step - loss: 0.4903 - accuracy: 0.8205 - val_loss: 0.4963 - val_accuracy: 0.8084\n",
      "epoch 25\n",
      "44/44 [==============================] - 3s 71ms/step - loss: 0.4690 - accuracy: 0.8198 - val_loss: 0.5352 - val_accuracy: 0.7987\n",
      "epoch 26\n",
      "44/44 [==============================] - 3s 77ms/step - loss: 0.4815 - accuracy: 0.8180 - val_loss: 0.5381 - val_accuracy: 0.7922\n",
      "epoch 27\n",
      "44/44 [==============================] - 3s 67ms/step - loss: 0.4613 - accuracy: 0.8278 - val_loss: 0.5472 - val_accuracy: 0.7987\n",
      "epoch 28\n",
      "44/44 [==============================] - 3s 70ms/step - loss: 0.4403 - accuracy: 0.8376 - val_loss: 0.4687 - val_accuracy: 0.8344\n",
      "epoch 29\n",
      "44/44 [==============================] - 3s 78ms/step - loss: 0.4368 - accuracy: 0.8415 - val_loss: 0.4469 - val_accuracy: 0.8539\n",
      "epoch 30\n",
      "44/44 [==============================] - 3s 66ms/step - loss: 0.4167 - accuracy: 0.8412 - val_loss: 0.4759 - val_accuracy: 0.8084\n",
      "epoch 31\n",
      "44/44 [==============================] - 3s 67ms/step - loss: 0.4291 - accuracy: 0.8484 - val_loss: 0.5017 - val_accuracy: 0.8019\n",
      "epoch 32\n",
      "44/44 [==============================] - 3s 66ms/step - loss: 0.4404 - accuracy: 0.8376 - val_loss: 0.5206 - val_accuracy: 0.8019\n",
      "epoch 33\n",
      "44/44 [==============================] - 3s 66ms/step - loss: 0.4172 - accuracy: 0.8484 - val_loss: 0.4899 - val_accuracy: 0.7987\n",
      "epoch 34\n",
      "44/44 [==============================] - 3s 66ms/step - loss: 0.4066 - accuracy: 0.8509 - val_loss: 0.4517 - val_accuracy: 0.8247\n",
      "epoch 35\n",
      "44/44 [==============================] - 4s 83ms/step - loss: 0.4039 - accuracy: 0.8495 - val_loss: 0.5555 - val_accuracy: 0.7922\n",
      "epoch 36\n",
      "44/44 [==============================] - 3s 64ms/step - loss: 0.4166 - accuracy: 0.8401 - val_loss: 0.4468 - val_accuracy: 0.8279\n",
      "epoch 37\n",
      "44/44 [==============================] - 3s 65ms/step - loss: 0.3999 - accuracy: 0.8542 - val_loss: 0.5910 - val_accuracy: 0.7857\n",
      "epoch 38\n",
      "44/44 [==============================] - 4s 83ms/step - loss: 0.4077 - accuracy: 0.8462 - val_loss: 0.4723 - val_accuracy: 0.8312\n",
      "epoch 39\n",
      "44/44 [==============================] - 3s 66ms/step - loss: 0.3787 - accuracy: 0.8622 - val_loss: 0.4541 - val_accuracy: 0.8247\n",
      "epoch 40\n",
      "44/44 [==============================] - 3s 65ms/step - loss: 0.3811 - accuracy: 0.8632 - val_loss: 0.5803 - val_accuracy: 0.7987\n",
      "epoch 41\n",
      "44/44 [==============================] - 3s 66ms/step - loss: 0.3770 - accuracy: 0.8546 - val_loss: 0.5204 - val_accuracy: 0.8182\n",
      "epoch 42\n",
      "44/44 [==============================] - 3s 65ms/step - loss: 0.3776 - accuracy: 0.8589 - val_loss: 0.4728 - val_accuracy: 0.8279\n",
      "epoch 43\n",
      "44/44 [==============================] - 3s 70ms/step - loss: 0.3514 - accuracy: 0.8712 - val_loss: 0.4878 - val_accuracy: 0.8084\n",
      "epoch 44\n",
      "44/44 [==============================] - 3s 76ms/step - loss: 0.3756 - accuracy: 0.8629 - val_loss: 0.4771 - val_accuracy: 0.8279\n",
      "epoch 45\n",
      "44/44 [==============================] - 3s 72ms/step - loss: 0.3725 - accuracy: 0.8542 - val_loss: 0.4522 - val_accuracy: 0.8182\n",
      "epoch 46\n",
      "44/44 [==============================] - 3s 65ms/step - loss: 0.3308 - accuracy: 0.8759 - val_loss: 0.3999 - val_accuracy: 0.8571\n",
      "epoch 47\n",
      "44/44 [==============================] - 3s 74ms/step - loss: 0.3556 - accuracy: 0.8694 - val_loss: 0.5372 - val_accuracy: 0.8052\n",
      "epoch 48\n",
      "44/44 [==============================] - 3s 66ms/step - loss: 0.3388 - accuracy: 0.8716 - val_loss: 0.4388 - val_accuracy: 0.8539\n",
      "epoch 49\n",
      "44/44 [==============================] - 3s 65ms/step - loss: 0.3495 - accuracy: 0.8708 - val_loss: 0.4904 - val_accuracy: 0.8149\n",
      "epoch 50\n",
      "44/44 [==============================] - 3s 68ms/step - loss: 0.3607 - accuracy: 0.8687 - val_loss: 0.5020 - val_accuracy: 0.8214\n",
      "epoch 51\n",
      "44/44 [==============================] - 3s 65ms/step - loss: 0.4169 - accuracy: 0.8452 - val_loss: 0.4922 - val_accuracy: 0.8214\n",
      "epoch 52\n",
      "44/44 [==============================] - 3s 65ms/step - loss: 0.3339 - accuracy: 0.8784 - val_loss: 0.4371 - val_accuracy: 0.8279\n",
      "epoch 53\n",
      "44/44 [==============================] - 3s 76ms/step - loss: 0.3277 - accuracy: 0.8842 - val_loss: 0.4591 - val_accuracy: 0.8344\n",
      "epoch 54\n",
      "44/44 [==============================] - 3s 72ms/step - loss: 0.3114 - accuracy: 0.8849 - val_loss: 0.4841 - val_accuracy: 0.8149\n",
      "epoch 55\n",
      "44/44 [==============================] - 3s 66ms/step - loss: 0.3063 - accuracy: 0.8875 - val_loss: 0.6245 - val_accuracy: 0.7760\n",
      "epoch 56\n",
      "44/44 [==============================] - 3s 66ms/step - loss: 0.3563 - accuracy: 0.8726 - val_loss: 0.4505 - val_accuracy: 0.8279\n",
      "epoch 57\n",
      "44/44 [==============================] - 3s 66ms/step - loss: 0.3081 - accuracy: 0.8875 - val_loss: 0.4274 - val_accuracy: 0.8506\n",
      "epoch 58\n",
      "44/44 [==============================] - 3s 66ms/step - loss: 0.3309 - accuracy: 0.8799 - val_loss: 0.5367 - val_accuracy: 0.7987\n",
      "epoch 59\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "44/44 [==============================] - 3s 66ms/step - loss: 0.3524 - accuracy: 0.8687 - val_loss: 0.4705 - val_accuracy: 0.8312\n",
      "epoch 60\n",
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      "epoch 67\n",
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      "epoch 69\n",
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      "epoch 70\n",
      "44/44 [==============================] - 4s 84ms/step - loss: 0.2792 - accuracy: 0.8991 - val_loss: 0.4308 - val_accuracy: 0.8279\n",
      "epoch 71\n",
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      "epoch 72\n",
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      "epoch 73\n",
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      "epoch 74\n",
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      "epoch 75\n",
      "44/44 [==============================] - 3s 64ms/step - loss: 0.2562 - accuracy: 0.9085 - val_loss: 0.4340 - val_accuracy: 0.8344\n",
      "epoch 76\n",
      "44/44 [==============================] - 3s 63ms/step - loss: 0.2710 - accuracy: 0.9009 - val_loss: 0.4236 - val_accuracy: 0.8442\n",
      "epoch 77\n",
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      "epoch 78\n",
      "44/44 [==============================] - 3s 67ms/step - loss: 0.2701 - accuracy: 0.8998 - val_loss: 0.5231 - val_accuracy: 0.8182\n",
      "epoch 79\n",
      "44/44 [==============================] - 3s 71ms/step - loss: 0.2722 - accuracy: 0.8973 - val_loss: 0.4390 - val_accuracy: 0.8312\n",
      "epoch 80\n",
      "44/44 [==============================] - 3s 69ms/step - loss: 0.2626 - accuracy: 0.9027 - val_loss: 0.4668 - val_accuracy: 0.8377\n",
      "epoch 81\n",
      "44/44 [==============================] - 2011s 47s/step - loss: 0.3028 - accuracy: 0.8897 - val_loss: 0.4501 - val_accuracy: 0.8409\n",
      "epoch 82\n",
      "44/44 [==============================] - 4s 86ms/step - loss: 0.2667 - accuracy: 0.9009 - val_loss: 0.4373 - val_accuracy: 0.8442\n",
      "epoch 83\n",
      "44/44 [==============================] - 4s 102ms/step - loss: 0.2581 - accuracy: 0.9001 - val_loss: 0.4695 - val_accuracy: 0.8214\n",
      "epoch 84\n",
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      "epoch 85\n",
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      "epoch 86\n",
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      "epoch 87\n",
      "44/44 [==============================] - 4s 89ms/step - loss: 0.2560 - accuracy: 0.9027 - val_loss: 0.4114 - val_accuracy: 0.8474\n",
      "epoch 88\n",
      "44/44 [==============================] - 4s 84ms/step - loss: 0.2452 - accuracy: 0.9096 - val_loss: 0.4682 - val_accuracy: 0.8214\n",
      "epoch 89\n",
      "44/44 [==============================] - 4s 82ms/step - loss: 0.2772 - accuracy: 0.8947 - val_loss: 0.4129 - val_accuracy: 0.8442\n",
      "epoch 90\n",
      "44/44 [==============================] - 4s 87ms/step - loss: 0.2675 - accuracy: 0.8951 - val_loss: 0.4249 - val_accuracy: 0.8377\n",
      "epoch 91\n",
      "44/44 [==============================] - 4s 82ms/step - loss: 0.2601 - accuracy: 0.9030 - val_loss: 0.4349 - val_accuracy: 0.8377\n",
      "epoch 92\n",
      "44/44 [==============================] - 3s 70ms/step - loss: 0.2167 - accuracy: 0.9222 - val_loss: 0.4320 - val_accuracy: 0.8312\n",
      "epoch 93\n",
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      "epoch 94\n",
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      "epoch 95\n",
      "44/44 [==============================] - 3s 69ms/step - loss: 0.2221 - accuracy: 0.9197 - val_loss: 0.4804 - val_accuracy: 0.8214\n",
      "epoch 96\n",
      "44/44 [==============================] - 3s 69ms/step - loss: 0.2249 - accuracy: 0.9186 - val_loss: 0.4294 - val_accuracy: 0.8474\n",
      "epoch 97\n",
      "44/44 [==============================] - 3s 68ms/step - loss: 0.2170 - accuracy: 0.9208 - val_loss: 0.4350 - val_accuracy: 0.8312\n",
      "epoch 98\n",
      "44/44 [==============================] - 3s 67ms/step - loss: 0.2402 - accuracy: 0.9092 - val_loss: 0.4954 - val_accuracy: 0.8409\n",
      "epoch 99\n",
      "44/44 [==============================] - 3s 68ms/step - loss: 0.2362 - accuracy: 0.9132 - val_loss: 0.4452 - val_accuracy: 0.8539\n",
      "epoch 100\n",
      "44/44 [==============================] - 3s 67ms/step - loss: 0.2234 - accuracy: 0.9222 - val_loss: 0.4207 - val_accuracy: 0.8506\n",
      "epoch 101\n",
      "44/44 [==============================] - 4s 90ms/step - loss: 0.2106 - accuracy: 0.9269 - val_loss: 0.4470 - val_accuracy: 0.8409\n",
      "epoch 102\n",
      "44/44 [==============================] - 4s 82ms/step - loss: 0.2170 - accuracy: 0.9204 - val_loss: 0.4057 - val_accuracy: 0.8571\n",
      "epoch 103\n",
      "44/44 [==============================] - 3s 68ms/step - loss: 0.2161 - accuracy: 0.9226 - val_loss: 0.4660 - val_accuracy: 0.8214\n",
      "epoch 104\n",
      "44/44 [==============================] - 4s 99ms/step - loss: 0.2153 - accuracy: 0.9208 - val_loss: 0.5245 - val_accuracy: 0.8247\n",
      "epoch 105\n",
      "44/44 [==============================] - 5s 104ms/step - loss: 0.2360 - accuracy: 0.9132 - val_loss: 0.4424 - val_accuracy: 0.8279\n",
      "epoch 106\n",
      "44/44 [==============================] - 4s 101ms/step - loss: 0.2270 - accuracy: 0.9153 - val_loss: 0.4479 - val_accuracy: 0.8474\n",
      "epoch 107\n",
      "44/44 [==============================] - 5s 105ms/step - loss: 0.1983 - accuracy: 0.9266 - val_loss: 0.4504 - val_accuracy: 0.8279\n",
      "epoch 108\n",
      "44/44 [==============================] - 3s 67ms/step - loss: 0.2078 - accuracy: 0.9200 - val_loss: 0.4436 - val_accuracy: 0.8539\n",
      "epoch 109\n",
      "44/44 [==============================] - 3s 68ms/step - loss: 0.2237 - accuracy: 0.9190 - val_loss: 0.4777 - val_accuracy: 0.8377\n",
      "epoch 110\n",
      "44/44 [==============================] - 3s 66ms/step - loss: 0.2478 - accuracy: 0.9114 - val_loss: 0.4621 - val_accuracy: 0.8312\n",
      "epoch 111\n",
      "44/44 [==============================] - 4s 82ms/step - loss: 0.2236 - accuracy: 0.9186 - val_loss: 0.4236 - val_accuracy: 0.8506\n",
      "epoch 112\n",
      "44/44 [==============================] - 3s 75ms/step - loss: 0.1880 - accuracy: 0.9338 - val_loss: 0.4344 - val_accuracy: 0.8377\n",
      "epoch 113\n",
      "44/44 [==============================] - 3s 64ms/step - loss: 0.1874 - accuracy: 0.9334 - val_loss: 0.3984 - val_accuracy: 0.8636\n",
      "epoch 114\n",
      "44/44 [==============================] - 3s 77ms/step - loss: 0.1762 - accuracy: 0.9370 - val_loss: 0.4148 - val_accuracy: 0.8442\n",
      "epoch 115\n",
      "44/44 [==============================] - 3s 73ms/step - loss: 0.1994 - accuracy: 0.9240 - val_loss: 0.4621 - val_accuracy: 0.8409\n",
      "epoch 116\n",
      "44/44 [==============================] - 3s 69ms/step - loss: 0.2082 - accuracy: 0.9255 - val_loss: 0.4364 - val_accuracy: 0.8474\n",
      "epoch 117\n",
      "44/44 [==============================] - 3s 63ms/step - loss: 0.2174 - accuracy: 0.9175 - val_loss: 0.4468 - val_accuracy: 0.8474\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 118\n",
      "44/44 [==============================] - 3s 64ms/step - loss: 0.1868 - accuracy: 0.9334 - val_loss: 0.4714 - val_accuracy: 0.8409\n",
      "epoch 119\n",
      "44/44 [==============================] - 4s 87ms/step - loss: 0.1920 - accuracy: 0.9309 - val_loss: 0.4677 - val_accuracy: 0.8312\n",
      "epoch 120\n",
      "44/44 [==============================] - 3s 71ms/step - loss: 0.2096 - accuracy: 0.9229 - val_loss: 0.4938 - val_accuracy: 0.8247\n",
      "epoch 121\n",
      "44/44 [==============================] - 3s 69ms/step - loss: 0.2446 - accuracy: 0.9088 - val_loss: 0.4649 - val_accuracy: 0.8409\n",
      "epoch 122\n",
      "44/44 [==============================] - 3s 63ms/step - loss: 0.2009 - accuracy: 0.9309 - val_loss: 0.5182 - val_accuracy: 0.8149\n",
      "epoch 123\n",
      "44/44 [==============================] - 3s 61ms/step - loss: 0.1867 - accuracy: 0.9374 - val_loss: 0.4335 - val_accuracy: 0.8571\n",
      "epoch 124\n",
      "44/44 [==============================] - 3s 61ms/step - loss: 0.1857 - accuracy: 0.9320 - val_loss: 0.4238 - val_accuracy: 0.8506\n",
      "epoch 125\n",
      "44/44 [==============================] - 3s 59ms/step - loss: 0.1859 - accuracy: 0.9327 - val_loss: 0.4717 - val_accuracy: 0.8409\n",
      "epoch 126\n",
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      "epoch 127\n",
      "44/44 [==============================] - 3s 58ms/step - loss: 0.2049 - accuracy: 0.9287 - val_loss: 0.4198 - val_accuracy: 0.8506\n",
      "epoch 128\n",
      "44/44 [==============================] - 3s 70ms/step - loss: 0.1890 - accuracy: 0.9295 - val_loss: 0.5179 - val_accuracy: 0.8377\n",
      "epoch 129\n",
      "44/44 [==============================] - 3s 67ms/step - loss: 0.1867 - accuracy: 0.9385 - val_loss: 0.4567 - val_accuracy: 0.8344\n",
      "epoch 130\n",
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      "epoch 131\n",
      "44/44 [==============================] - 3s 64ms/step - loss: 0.1754 - accuracy: 0.9342 - val_loss: 0.6289 - val_accuracy: 0.8019\n",
      "epoch 132\n",
      "44/44 [==============================] - 3s 71ms/step - loss: 0.2002 - accuracy: 0.9226 - val_loss: 0.5326 - val_accuracy: 0.8279\n",
      "epoch 133\n",
      "44/44 [==============================] - 3s 59ms/step - loss: 0.1893 - accuracy: 0.9291 - val_loss: 0.5408 - val_accuracy: 0.8279\n",
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      "epoch 135\n",
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      "epoch 136\n",
      "44/44 [==============================] - 3s 59ms/step - loss: 0.1921 - accuracy: 0.9367 - val_loss: 0.4391 - val_accuracy: 0.8604\n",
      "epoch 137\n",
      "44/44 [==============================] - 3s 59ms/step - loss: 0.2146 - accuracy: 0.9204 - val_loss: 0.4633 - val_accuracy: 0.8409\n",
      "epoch 138\n",
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      "epoch 139\n",
      "44/44 [==============================] - 3s 72ms/step - loss: 0.1672 - accuracy: 0.9396 - val_loss: 0.4165 - val_accuracy: 0.8669\n",
      "epoch 140\n",
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      "epoch 141\n",
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      "epoch 142\n",
      "44/44 [==============================] - 3s 77ms/step - loss: 0.1734 - accuracy: 0.9363 - val_loss: 0.5295 - val_accuracy: 0.8247\n",
      "epoch 143\n",
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      "epoch 144\n",
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      "epoch 146\n",
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      "epoch 147\n",
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      "epoch 149\n",
      "44/44 [==============================] - 3s 64ms/step - loss: 0.1713 - accuracy: 0.9389 - val_loss: 0.4828 - val_accuracy: 0.8506\n",
      "epoch 150\n",
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      "epoch 154\n",
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      "epoch 176\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "44/44 [==============================] - 3s 61ms/step - loss: 0.1372 - accuracy: 0.9537 - val_loss: 0.5014 - val_accuracy: 0.8506\n",
      "epoch 177\n",
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      "epoch 183\n",
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      "epoch 184\n",
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      "epoch 185\n",
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      "epoch 186\n",
      "44/44 [==============================] - 3s 59ms/step - loss: 0.1662 - accuracy: 0.9367 - val_loss: 0.4513 - val_accuracy: 0.8442\n",
      "epoch 187\n",
      "44/44 [==============================] - 3s 70ms/step - loss: 0.1322 - accuracy: 0.9519 - val_loss: 0.4395 - val_accuracy: 0.8604\n",
      "epoch 188\n",
      "44/44 [==============================] - 3s 69ms/step - loss: 0.1137 - accuracy: 0.9595 - val_loss: 0.5433 - val_accuracy: 0.8409\n",
      "epoch 189\n",
      "44/44 [==============================] - 3s 62ms/step - loss: 0.1383 - accuracy: 0.9483 - val_loss: 0.5349 - val_accuracy: 0.8377\n",
      "epoch 190\n",
      "44/44 [==============================] - 3s 59ms/step - loss: 0.1355 - accuracy: 0.9501 - val_loss: 0.4766 - val_accuracy: 0.8506\n",
      "epoch 191\n",
      "44/44 [==============================] - 3s 59ms/step - loss: 0.1303 - accuracy: 0.9526 - val_loss: 0.4812 - val_accuracy: 0.8571\n",
      "epoch 192\n",
      "44/44 [==============================] - 3s 74ms/step - loss: 0.1383 - accuracy: 0.9493 - val_loss: 0.5237 - val_accuracy: 0.8636\n",
      "epoch 193\n",
      "44/44 [==============================] - 3s 63ms/step - loss: 0.1123 - accuracy: 0.9584 - val_loss: 0.4459 - val_accuracy: 0.8442\n",
      "epoch 194\n",
      "44/44 [==============================] - 3s 71ms/step - loss: 0.1261 - accuracy: 0.9566 - val_loss: 0.4815 - val_accuracy: 0.8539\n",
      "epoch 195\n",
      "44/44 [==============================] - 3s 74ms/step - loss: 0.1315 - accuracy: 0.9493 - val_loss: 0.4524 - val_accuracy: 0.8734\n",
      "epoch 196\n",
      "44/44 [==============================] - 3s 59ms/step - loss: 0.1733 - accuracy: 0.9345 - val_loss: 0.5255 - val_accuracy: 0.8539\n",
      "epoch 197\n",
      "44/44 [==============================] - 3s 59ms/step - loss: 0.1499 - accuracy: 0.9410 - val_loss: 0.4673 - val_accuracy: 0.8604\n",
      "epoch 198\n",
      "44/44 [==============================] - 3s 59ms/step - loss: 0.1210 - accuracy: 0.9559 - val_loss: 0.5389 - val_accuracy: 0.8442\n",
      "epoch 199\n",
      "44/44 [==============================] - 3s 63ms/step - loss: 0.1133 - accuracy: 0.9595 - val_loss: 0.4608 - val_accuracy: 0.8701\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(epoch_save+1, 200): #change 100 to a larger number if necessary\n",
    "    print('epoch', epoch)\n",
    "    #set epochs=1\n",
    "    history=model.fit(X_train, Y_train, batch_size=64, epochs=1, validation_data=(X_val, Y_val))\n",
    "    loss_train_list.extend(history.history['loss'])\n",
    "    loss_val_list.extend(history.history['val_loss'])\n",
    "    acc_train_list.extend(history.history['accuracy'])\n",
    "    acc_val_list.extend(history.history['val_accuracy'])\n",
    "    #save the model to a file \n",
    "    model.save('ECG_Keras_sCE_e'+str(epoch)+'.keras')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XEVFA7sxOZjS"
   },
   "source": [
    "### Train the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FBSDSgFfOZjT"
   },
   "source": [
    "### Plot training loss vs epoch and validation loss vs epoch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-KRkqHpuOZjT",
    "outputId": "3d87e5df-b3ee-4c8a-f48f-c3c318946105"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history.history.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 393
    },
    "id": "xsc9XDhROZjT",
    "outputId": "16fa3447-738b-4b6f-8598-e5b44a2cad95"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(12,6))\n",
    "ax[0].plot(np.arange(0,len(loss_train_list)), loss_train_list, '-b', label='training loss')\n",
    "ax[0].plot(np.arange(0,len(loss_val_list)), loss_val_list, '-r', label='validation loss')\n",
    "ax[0].set_xlabel('epoch',fontsize=16)\n",
    "ax[0].legend(fontsize=16)\n",
    "ax[0].grid(True)\n",
    "ax[1].plot(np.arange(0,len(acc_train_list)), acc_train_list, '-b', label='training accuracy')\n",
    "ax[1].plot(np.arange(0,len(acc_val_list)), acc_val_list, '-r', label='validation accuracy')\n",
    "ax[1].set_xlabel('epoch',fontsize=16)\n",
    "ax[1].legend(fontsize=16)\n",
    "ax[1].grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tRXmqXsiOZjU"
   },
   "source": [
    "### Test the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_R3FxdyLt-Rd",
    "outputId": "4e4c44e0-9816-4a4e-a925-1855d2f9a273"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "144"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#load the best model\n",
    "best_epoch=np.argmax(acc_val_list)\n",
    "best_epoch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "id": "GMUgAglLuP1l"
   },
   "outputs": [],
   "source": [
    "import tensorflow\n",
    "model = tensorflow.keras.models.load_model(\"ECG_Keras_sCE_e\"+str(best_epoch)+\".keras\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kvtKs2SOOZjU",
    "outputId": "7bef3e93-a149-4504-c72f-892a02ce4664"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 0.27709969878196716\n",
      "Test accuracy: 0.9076722860336304\n"
     ]
    }
   ],
   "source": [
    "score = model.evaluate(X_test, Y_test, batch_size=64, verbose=0)\n",
    "print('Test loss:', score[0])\n",
    "print('Test accuracy:', score[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XJOCI3OhOZjU"
   },
   "source": [
    "### Make Prediction on the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "RZUvb8pVOZjU",
    "outputId": "d2006b58-9955-4c32-b6c9-8cbc728a01a6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13/13 [==============================] - 1s 22ms/step\n"
     ]
    }
   ],
   "source": [
    "Y_test_pred=model.predict(X_test, batch_size=64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zvgO9El9QfrW",
    "outputId": "707e96ed-ee0e-4f58-8d0e-7d66f217588c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.5425581e-04, 9.9974585e-01, 3.3115519e-09, 6.0984671e-09,\n",
       "       3.7224218e-15], dtype=float32)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test_pred[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_epAiYquOZjV",
    "outputId": "36e1beb1-2bd1-4e78-f795-61890f876d58"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(Y_test_pred[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Gb6ZtDyDOZjV",
    "outputId": "f8404488-986a-4194-c096-d1a35b72aa66"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "id": "MAt-AFp1wKhU"
   },
   "outputs": [],
   "source": [
    "Y_test_pred=np.argmax(Y_test_pred, axis=1)\n",
    "print('mah')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ql1Ui3ekv92b",
    "outputId": "6986a16a-96b8-49d3-f17e-5b8fff0e59be"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.84      0.84       176\n",
      "           1       0.87      0.88      0.88       154\n",
      "           2       0.95      0.92      0.93       146\n",
      "           3       0.90      0.93      0.92       122\n",
      "           4       0.97      0.97      0.97       171\n",
      "\n",
      "    accuracy                           0.91       769\n",
      "   macro avg       0.91      0.91      0.91       769\n",
      "weighted avg       0.91      0.91      0.91       769\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(Y_test, Y_test_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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